WO2011114516A1 - Paper sheet identification device and paper sheet identification method - Google Patents

Paper sheet identification device and paper sheet identification method Download PDF

Info

Publication number
WO2011114516A1
WO2011114516A1 PCT/JP2010/054829 JP2010054829W WO2011114516A1 WO 2011114516 A1 WO2011114516 A1 WO 2011114516A1 JP 2010054829 W JP2010054829 W JP 2010054829W WO 2011114516 A1 WO2011114516 A1 WO 2011114516A1
Authority
WO
WIPO (PCT)
Prior art keywords
image data
paper sheet
discrimination
threshold
threshold value
Prior art date
Application number
PCT/JP2010/054829
Other languages
French (fr)
Japanese (ja)
Inventor
正範 坪田
賢二 山本
浩貴 坪田
Original Assignee
グローリー株式会社
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by グローリー株式会社 filed Critical グローリー株式会社
Priority to PCT/JP2010/054829 priority Critical patent/WO2011114516A1/en
Publication of WO2011114516A1 publication Critical patent/WO2011114516A1/en

Links

Images

Classifications

    • G—PHYSICS
    • G07—CHECKING-DEVICES
    • G07D—HANDLING OF COINS OR VALUABLE PAPERS, e.g. TESTING, SORTING BY DENOMINATIONS, COUNTING, DISPENSING, CHANGING OR DEPOSITING
    • G07D7/00—Testing specially adapted to determine the identity or genuineness of valuable papers or for segregating those which are unacceptable, e.g. banknotes that are alien to a currency
    • G07D7/20—Testing patterns thereon
    • G07D7/2075—Setting acceptance levels or parameters

Definitions

  • the present invention relates to a paper sheet discriminating apparatus and a paper sheet discriminating method for discriminating whether a paper sheet is correct or not, and in particular, a paper sheet discriminating apparatus capable of easily and appropriately setting a parameter for discriminating damage. And a paper sheet discrimination method.
  • a paper sheet discriminating apparatus that discriminates whether paper sheets such as banknotes and gift certificates are correct or not is known.
  • a non-performing ticket level indicating the degree of a non-performing ticket is provided in multiple stages, and the number of non-performing slips is determined by determining the pertinent slip level corresponding to the paper sheet to be determined. Is disclosed.
  • Patent Document 2 discloses a paper sheet discriminating apparatus that displays a schematic image representing a discrimination item corresponding to a parameter on a parameter change screen. For example, in the paper sheet discriminating apparatus disclosed in Patent Document 2, when a parameter for “corner break” is set, an image indicating a corner break state is displayed.
  • Patent Document 1 presents a guideline for resetting parameters only after discrimination processing for a large number of paper sheets is completed, the time required for parameter adjustment is prolonged and complicated. There was a problem that forced adjustment work was forced on the operator. For this reason, there is a problem that it is difficult to appropriately adjust the parameters.
  • the paper sheet identification device of Patent Document 2 merely notifies the operator of which determination item the parameter to be adjusted corresponds to, and what determination result is obtained after parameter adjustment. Not a notification.
  • the present invention has been made to solve the above-described problems of the prior art, and provides a paper sheet discriminating apparatus and a paper sheet discriminating method capable of easily and appropriately setting a parameter for discriminating damage.
  • the purpose is to provide.
  • the present invention is a paper sheet discriminating apparatus for discriminating whether a paper sheet is normal or not, and includes a storage means for storing image data and a discrimination item. Based on the image data extracted by using the threshold value, an example of the result of determining whether the threshold value after the setting operation received by the receiving unit and the threshold value after the setting operation received by the receiving unit is used. And an example means for performing the above.
  • the example means stores the image data that is an example of correct determination and / or the image data that is an example of loss determination based on the threshold value after the setting operation. It is characterized by extracting from the means.
  • the present invention is characterized in that, in the above-mentioned invention, the storage means stores imaging data obtained by imaging a paper sheet as the image data.
  • the present invention is the above invention, wherein the storage means stores the image data in which a comparison value to be compared with the threshold value is associated with each of the determination items, and the example means has the threshold value equal to or higher than the threshold value.
  • the image data associated with the comparison value and / or the image data associated with the comparison value less than the threshold value are extracted for each of the determination items, and the image data together with the extracted image data is positively determined. It is characterized by displaying the distinction of whether it is an example of the above or an example of loss determination.
  • the present invention further includes a determination unit that determines whether the paper sheet is normal or not by using the threshold value.
  • the determination unit receives the image data as an input, and the determination is performed on the image data.
  • the comparison value calculated for each item is stored in the storage unit in association with the image data.
  • the example means when there is no image data having the comparison value to be exemplified for the specific discrimination item, the example means has the comparison value different for the discrimination item.
  • the image data to be exemplified is generated from the image data.
  • the present invention is characterized in that, in the above invention, the example means exemplifies composite data obtained by combining the image data extracted for each of the different discrimination items.
  • the present invention is also a paper sheet discrimination method for discriminating whether a paper sheet is normal or not, and a storage process for storing image data in a storage unit and a reception operation for accepting a setting operation for a threshold prepared for each discrimination item. And an example step of performing an example of the result of damage determination when the threshold value after the setting operation received by the receiving step is used based on the image data extracted using the threshold value. It is characterized by.
  • image data is stored, a setting operation with respect to a threshold value prepared for each determination item is received, and an example of a fitness determination result when the received threshold value after the setting operation is used is illustrated after the setting operation. Since the determination is made based on the image data extracted using the threshold value, it is easy to set the damage determination parameter by exemplifying the damage determination result with the image data corresponding to the threshold value after the setting operation. And there is an effect that it can be appropriately performed.
  • image data that is an example of correct discrimination and / or image data that is an example of loss discrimination are extracted based on the threshold value after the setting operation. Is positively determined, and it is possible to accurately tell to the operator what state of the non-performing slip is determined.
  • the imaging data obtained by imaging the paper sheet is stored as the image data, so that the state of the damaged ticket is determined correctly, and the state of the damaged ticket is determined as lost. This is advantageous in that it can be conveyed to the operator more accurately by exemplifying the actual image.
  • the image data associated with the comparison value equal to or greater than the threshold value and / or the image data associated with the comparison value to be compared with the threshold value are stored for each discrimination item. Extracting image data associated with a comparison value less than the threshold value for each discrimination item, and displaying with the extracted image data whether the image data is an example of correct discrimination or an example of loss discrimination As a result, the determination result obtained by changing the threshold value can be accurately and promptly transmitted to the operator.
  • the image data is received as input in the determination of the normality, and the received image data is calculated for each determination item. Since the comparison value is stored in association with the image data, there is an effect that it is possible to extract an appropriate example image even when there is an individual difference in devices used for damage determination.
  • image data to be exemplified is generated from image data having a different comparison value for the discrimination item. Therefore, by providing an exemplary image in which a plurality of discrimination items are combined, there is an effect that it is possible to more easily and appropriately set the parameter for damage discrimination.
  • the exemplary image is displayed even when there is no actual image data to be displayed. There is an effect that it can be provided.
  • FIG. 1 is a diagram showing an outline of a paper sheet discrimination method according to the present invention.
  • FIG. 2 is a block diagram illustrating the configuration of the paper sheet discriminating apparatus according to the present embodiment.
  • FIG. 3 is a diagram showing an internal configuration of the banknote handling apparatus.
  • FIG. 4 is a diagram illustrating an operation example of the banknote handling apparatus.
  • FIG. 5 is a diagram showing an outline of the learning process.
  • FIG. 6 is a diagram illustrating an example of the reference threshold information and the exemplary image information.
  • FIG. 7 is a diagram illustrating an example of the selection screen.
  • FIG. 8 is a diagram illustrating an example of a fine adjustment screen.
  • FIG. 9 is a diagram illustrating a modification of the fine adjustment screen.
  • FIG. 10 is a diagram illustrating a modification of the threshold setting screen.
  • FIG. 11 is a flowchart illustrating the processing procedure of the learning process.
  • FIG. 12 is a flowchart showing the processing procedure of the threshold adjustment processing.
  • FIG. 1 is a diagram showing an outline of a paper sheet discrimination method according to the present invention.
  • the paper sheet discriminating method according to the present invention creates a database of the actual images of the acquired slips in advance, and corresponds to the changed threshold when setting the threshold for discriminating damage.
  • the main feature is that an actual image to be extracted is illustrated from a database.
  • the correct note group predetermined number of correct notes
  • each non-issued ticket group prepared for each level of non-issued ticket (in the figure, level A and level B).
  • “level” refers to each numerical range obtained by dividing a comparison value to be compared with a threshold value into a plurality of stages for each of a plurality of discrimination items used for discrimination of fitness.
  • the “reference threshold value” refers to a threshold value set as an initial value, and has a plurality of sets in which the reference threshold value corresponding to each discrimination item is combined for all discrimination items. Then, the operator selects which set is used as a default value.
  • which set is used as a default value may be set in advance at the time of shipment of the apparatus.
  • a set used as a default value may be set for each country or region to which the device is shipped.
  • an example image DB (database) is generated by actually imaging the slips included in each banknote group (see (A-2) in the figure).
  • the example image DB stores information in which the comparison value of each discrimination item is associated with the image data.
  • the reference threshold value selected from the reference threshold value DB is displayed as a default value on the threshold value adjustment screen for adjusting the threshold value (see (B) in the figure). Further, in conjunction with the threshold changing operation, the process of extracting the “exemplary image that has cleared the threshold” and the “exemplary image that has not cleared the threshold” from the exemplary image DB is repeated ((C) in the figure). reference).
  • the fact that the threshold has been cleared together with the “example image that has cleared the threshold” indicates that the example image has not cleared the threshold.
  • the fact that the threshold value has not been cleared is displayed.
  • a paper sheet discriminating apparatus to which the paper sheet discriminating method described with reference to FIG. 1 is applied will be described.
  • a paper sheet discriminating apparatus having a function for discriminating damage is described.
  • the present invention is applied to an apparatus having only a threshold setting function (for example, a threshold setting apparatus). Also good.
  • FIG. 2 is a block diagram showing the configuration of the paper sheet discriminating apparatus 10 according to the present embodiment.
  • the paper sheet discriminating apparatus 10 includes an identification unit 11, a display unit 12, an input unit 13, a control unit 14, and a storage unit 15.
  • the control unit 14 further includes a learning unit 14a, an example unit 14b, a threshold adjustment unit 14c, an identification data acquisition unit 14d, and a fitness determination unit 14e.
  • the storage unit 15 stores reference threshold information 15a, exemplary image information 15b, and adjustment threshold information 15c.
  • the identification unit 11 is a device that performs processing for discriminating the type of paper sheet and the correctness of the paper sheet using an image sensor and a magnetic sensor.
  • the identification unit 11 associates the comparison value acquired for each discrimination item with the image data obtained by imaging the paper sheet, and then the control unit 14 learning units 14a. Further, when the paper sheet discriminating apparatus 10 operates in the discrimination mode, the identification unit 11 notifies the identification data acquisition unit 14d of the control unit 14 of the comparison value acquired for each discrimination item.
  • the display unit 12 is a display device such as a liquid crystal display, and displays a threshold setting screen or the like generated by the example unit 14b of the control unit 14.
  • the input unit 13 is an input device such as a numeric keypad, and notifies the threshold adjustment unit 14c of the control unit 14 of the operation content by the operator.
  • a device having both functions for example, a touch panel display may be used.
  • FIG. 3 is a diagram illustrating an internal configuration of the banknote handling apparatus 100
  • FIG. 4 is a diagram illustrating an operation example of the banknote handling apparatus 100.
  • the banknote handling apparatus 100 includes a depositing unit 101, a first dispensing unit 102a, a second dispensing unit 102b, a temporary holding unit 102c, a transport unit 103, an identification unit 104, An operation display unit 105 and a controller 106 are provided.
  • the banknote handling apparatus 100 includes a scrutiny cassette 107, a stacker 108a, a stacker 108b, a stacker 108c, a stacker 108d, a stacker 108e, and a collection cassette 109.
  • the identification unit 104 corresponds to the identification unit 11 in FIG. 2
  • the operation display unit 105 corresponds to the display unit 12 and the input unit 13
  • the controller 106 corresponds to the control unit 14 and the storage unit 15.
  • the controller 106 includes a communication interface with other apparatuses and a printer that performs receipt printing.
  • the depositing unit 101 is a device that accepts bills inserted by a user or an operator, and the bills deposited into the depositing unit 101 are sent out to the transport unit 103.
  • the first withdrawal unit 102 a and the second withdrawal unit 102 b are devices that withdraw banknotes received from the transport unit 103.
  • the temporary storage unit 102c is a device that temporarily holds banknotes deposited from the depositing unit 101 and banknotes fed out from the stackers (108a to 108e).
  • the transport unit 103 is a device that transports the banknote deposited from the deposit unit 101 to the temporary storage unit 102c and each stacker (108a to 108e) via the identification unit 104. Further, the transport unit 103 transports banknotes fed from the stackers (108a to 108e) to the first withdrawal unit 102a and the second withdrawal unit 102b.
  • the stacker 108a, the stacker 108b, the stacker 108c, the stacker 108d, and the stacker 108e are storage units prepared for respective denominations, for example, when the banknotes received from the transport unit 103 are accumulated and a feeding instruction is received. Feeds the designated number of banknotes to the transport unit 103.
  • the scrutiny cassette 107 is a cassette used when re-identifying banknotes accumulated in the stackers (108a to 108e). For example, when a failure such as banknote jam occurs in the stacker 108d and is recovered, all banknotes are fed out from the stacker 108d and stored in the scrutinizing cassette 107 through re-identification by the identification unit 104.
  • the re-identified banknotes stored in the scrutiny cassette 107 are returned to the stacker 108d via the transport unit 103.
  • the collection cassette 109 is a detachable storage unit used when collecting banknotes from the banknote processing apparatus 100.
  • FIG. 4A shows an operation example at the time of depositing
  • FIG. 4B shows an operation example at the time of withdrawal.
  • FIG. 4A when a banknote is deposited into the depositing unit 101 by a user or an operator (see (A-1) in FIG. 4), the transport unit 103 identifies the deposited banknote as the identifying unit 104.
  • the identification unit 104 executes bill recognition processing (see (A-2) in the figure).
  • banknotes identified as correct bills by the identifying unit 104 are temporarily stored in the temporary holding unit 102c ((A-3a) in the figure).
  • banknotes identified by the identifying unit 104 as other than genuine (for example, counterfeit or damaged) are returned to the second dispensing unit 102b (see (A-3b) in the figure).
  • the genuine bills once stored in the temporary holding unit 102c are respectively stored in the corresponding stackers (any one of 108a to 108e) when the operation display unit 105 accepts the confirmation operation ((A -4)). Then, the stock amount (in this case, addition) of the stacker (any one of 108a to 108e) storing the correct ticket is updated (see (A-5) in the figure).
  • the temporary holding unit 102c may not be used.
  • the banknotes identified as correct bills by the identifying unit 104 are respectively stored in the corresponding stackers (any one of 108a to 108e) without going through the temporary holding unit 102c.
  • the banknotes identified by the identifying unit 104 as other than the regular bill for example, a fake ticket or a non-performing bill
  • the stock amount of the stacker any one of 108a to 108e in which the correct ticket is stored is updated.
  • FIG. 4B when the operation display unit 105 accepts a withdrawal operation (see (B-1) in FIG. 4), a predetermined number of banknotes are paid out from the corresponding stackers (108a to 108e). (See (B-2) in the figure). The fed banknotes are withdrawn to the first withdrawal unit 102a via the transport unit 103 (see (B-3) in the figure).
  • the bills fed out of the stackers (108a to 108e) may be re-identified by the identification unit 104.
  • the banknote in which the abnormality is found in the identification unit 104 is temporarily stored in the temporary storage unit 102c, and returned to the feeding stacker (108a to 108e) at a predetermined timing.
  • the control unit 14 is a control unit that performs overall control of the paper sheet discriminating apparatus 10.
  • the learning unit 14a receives and stores the comparison value acquired for each discrimination item and the image data obtained by imaging the paper sheet from the discrimination unit 11. It is a processing unit that performs processing for generating the reference threshold information 15a and the exemplary image information 15b of the unit 15.
  • the learning unit 14a uses the correct ticket group (predetermined number of correct tickets), and learning using each of the damaged ticket groups (level A and level B in the figure) prepared for each level of the damaged ticket.
  • the reference threshold value information 15a including the reference threshold value.
  • three types of strong, medium, and weak are generated as the reference threshold.
  • the learning unit 14a generates, for each banknote, exemplary image information 15b in which each image data obtained by capturing each banknote is associated with each comparison value acquired by the identification unit 11 for each discrimination item.
  • the learning unit 14a synthesizes the image data corresponding to the comparison values other than the missing comparison value to thereby obtain the missing comparison value.
  • generates the image data corresponding to is performed.
  • the learning unit 14a combines the image data with the comparison values “5” and “7” to generate image data with the comparison value “6”.
  • the image data corresponding to the missing comparison value is not generated in advance by the learning unit 14a, but may be generated, for example, after receiving a threshold change operation when displaying the image data. In this case, if there is no image data to be exemplified, the image data to be exemplified may be generated by combining a plurality of image data corresponding to other comparison values.
  • FIG. 5 is a diagram showing an outline of the learning process.
  • (A) in the figure shows a basic example of the learning process
  • (B) in the figure shows an example of threshold estimation in the case where there is no set of a predetermined banknote level.
  • the horizontal axis of each graph shown in the figure represents the comparison value for each discrimination item
  • the vertical axis represents the distribution frequency (for example, the number of paper sheets).
  • the learning unit 14a generates a correct distribution based on the identification result identified by the identifying unit 11 with respect to a predetermined number of correct tickets (see the broken line in FIG. 5).
  • the correct note distribution is, for example, a normal distribution.
  • the learning unit 14a generates each banknote distribution based on the identification result identified by the identification unit 11 for each banknote group consisting of a predetermined number for each banknote level.
  • the figure shows a case where a non-ticket group at a non-slip level A, a non-slip group at a non-slip level B and a non-slip group at a non-slip level C are used. It is assumed that the level of the loss ticket is the worst in level C and the closest to the correct ticket in level A.
  • the learning unit 14a generates a loss distribution for each of a plurality of loss ticket levels. Then, the learning unit 14a determines a threshold A for the slipped ticket level A, a threshold B for the slipped ticket level B, and a threshold C for the slipped ticket level C. In the figure, a case is shown in which each threshold value is determined near the apex of each banknote distribution. Then, the determined threshold A, threshold B, and threshold C are used as the above-described reference threshold.
  • the threshold value A corresponding to the loss ticket level A is used as the reference threshold value
  • about 30% of all banknotes are determined as the loss tickets in the above-described determination mode
  • the threshold value B is used as the reference threshold value.
  • about 10% is determined to be a non-performing ticket.
  • the threshold value C is used as the reference threshold value, about 3% is determined to be a bad ticket.
  • threshold value among the threshold value A, threshold value B, and threshold value C is selected as the reference threshold value, the tendency of the damage discrimination in the paper sheet discriminating apparatus 10 can be changed.
  • the learning part 14a performs the process which estimates the threshold value corresponding to the missing banknote level based on the banknote distribution corresponding to another banknote level.
  • the learning unit 14a corresponds to the threshold A corresponding to the loss ticket level A and the loss ticket level B when there is no loss ticket group of the loss ticket level B.
  • the threshold value B corresponding to the loss distribution at the loss ticket level B is estimated (see (B-1) in the figure).
  • the learning unit 14a An average value with C is defined as a threshold value B.
  • the threshold corresponding to the non-ticket level B is set to the non-ticket level A and C. The case of estimation by interpolation using the distribution of is shown.
  • the present invention is not limited to this, and the threshold value corresponding to the missing banknote level may be estimated by extrapolation. For example, when there is a population set corresponding to the loss ticket levels A and B and there is no population set corresponding to the loss ticket level C, the loss tickets are obtained by extrapolation using the distribution of the loss ticket levels A and B.
  • the threshold value corresponding to level C may be estimated.
  • FIG. 6 is a diagram illustrating an example of the reference threshold information 15a and the exemplary image information 15b. Note that (A) in the figure shows an example of the reference threshold information 15a, and (B) in the figure shows an example of the illustrative image information 15b. Moreover, in the same figure, the example in case the paper sheets used as discrimination
  • the reference threshold value information 15a includes a “denomination” item, a “defect ticket level” item, and a “reference threshold” item.
  • the “reference threshold value” item is classified for each discrimination item in the damage discrimination.
  • the reference threshold information 15a stores a reference threshold for each of a plurality of banknote levels for one denomination.
  • the same figure has illustrated the case where the reference threshold value corresponding to each of the banknote level A, banknote level B, and banknote level C is stored about one denomination.
  • the reference threshold values are “50” for the “tape ticket” item, “60” for the “tear” item, and “70” for the “dirt (part)” item.
  • ”And“ dirt (whole) ”items are“ 100 ”
  • “ graffiti ”items are“ 150 ”
  • “ blur ”items are“ 200 ”.
  • Each reference threshold is set by the learning unit 14a.
  • the reference threshold value information 15a is information in which reference threshold values corresponding to a plurality of discrimination items are stored for each denomination and each non-performing ticket level. If the default level B is set for the denomination ⁇ , “60”, “70”, “80”, “110”, “160”, and “210” are used as the respective reference threshold values. Will be.
  • the exemplary image information 15b includes an “image ID” item and a “comparison value” item.
  • a combination of numbers and symbols that uniquely identify image data can be used for the “image ID” item, but a serial number (an identification code printed on the banknote) read from the banknote is used. It is good as well.
  • the “comparison value” item is classified for each discrimination item in the fitness discrimination. Note that the contents of the classification are the same as those in FIG. 6A, and a description thereof is omitted here.
  • the comparison values corresponding to the image data whose “image ID” item is “11111” are “10”, “20”, “30”, “40”, “50”, and “60”. Further, the comparison values corresponding to the image data whose “image ID” item is “22222” are “20”, “30”, “40”, “50”, “60”, and “70”. Each comparison value is set by the learning unit 14a.
  • FIG. 6B shows a case where the image data itself is not included in the exemplary image information 15b, the image data itself may be included in the exemplary image information 15b.
  • the example unit 14b When receiving the threshold adjustment instruction via the input unit 13, the example unit 14b extracts a predetermined number of image data that clears the threshold and image data that does not clear the threshold from the example image information 15b using the adjusted threshold. It is a processing part which performs processing to perform.
  • the example unit 14b also performs a process of displaying a screen including the extracted image data on the display unit 12.
  • the example unit 14b uses the reference threshold value of the reference threshold value information 15a as the initial value of the threshold value on the threshold adjustment screen.
  • An example of the screen generated by the example unit 14b will be described later with reference to FIGS.
  • the threshold adjustment unit 14c is a processing unit that performs processing for storing the adjusted threshold value in the storage unit 15 as the adjustment threshold information 15c when an instruction to change the threshold value is received from the input unit 13.
  • the configuration of the adjustment threshold information 15c is the same as the reference threshold information 15a shown in FIG. Each time the adjustment threshold information 15c is updated, re-extraction processing of the exemplary image information 15b and update processing of the threshold adjustment screen are performed by the exemplary unit 14b.
  • the identification data acquisition unit 14d receives the comparison value acquired for each discrimination item from the discrimination unit 11 and the received comparison value to the fitness discrimination unit 14e. It is a processing unit that performs a passing process.
  • the damage determination unit 14e is a processing unit that determines the damage of the paper sheet by comparing the comparison value received from the identification data acquisition unit 14d with the finally determined adjustment threshold information 15c. is there.
  • the storage unit 15 is a storage device such as a hard disk drive or a non-volatile memory, and stores reference threshold information 15a, exemplary image information 15b, and adjustment threshold information 15c.
  • the contents of the reference threshold information 15a, the exemplary image information 15b, and the adjustment threshold information 15c have already been described with reference to FIG.
  • FIG. 7 is a diagram illustrating an example of the selection screen. Note that (A) in the figure shows a denomination selection screen 71 and (B) in the figure shows an item selection screen 72, respectively.
  • the denomination selection screen 71 has a denomination selection button 71a prepared for each denomination. For example, when the denomination selection button 71a corresponding to “denomination ⁇ ” is selected, the item selection screen 72 shown in FIG. 7B is displayed.
  • the item selection screen 72 includes a determination item selection button 72a corresponding to a determination item for fitness determination, and a threshold area 72b in which a reference threshold value in the determination item is displayed as an initial value. There are as many sets as there are discriminating items.
  • the item selection screen 72 also displays a denomination (“denomination ⁇ ” in the figure) to be finely adjusted.
  • FIG. 8 is a diagram illustrating an example of the fine adjustment screen 81. Note that (A) in the figure shows an example of the fine adjustment screen 81 displayed first, and (B) in the figure shows an example of the fine adjustment screen 81 after acceptance of the threshold change operation. Yes.
  • the fine adjustment screen 81 includes an information area 82 in which denominations and discrimination items that are targets for fine adjustment of the threshold are displayed, an up button 83a that receives an operation for increasing the threshold, A down button 83b for accepting an operation for reducing the threshold value, a threshold value display area 84 for displaying the latest threshold value, an example image area 85 for displaying an example image, and a confirm button 86 for pressing the threshold value have.
  • two of the four example images in which “ ⁇ ” is displayed have an example image having a comparison value equal to or less than the threshold value “50”, that is, the threshold value. It is the image for illustration which cleared.
  • Two of the “x” displayed are example images having comparison values larger than the threshold value “50”, that is, example images that have not cleared the threshold value.
  • the comparison values of the image for illustration are, for example, “35”, “45”, “55”, “65” in order from the top of the figure.
  • the comparison value “45” among the example images included in the example image information 15b, a plurality of example images having comparison values close to the comparison value “45” are selected. What is necessary is just to synthesize
  • the conditions for extracting the example image having what kind of comparison value can be determined as appropriate.
  • the new image threshold value “55” is used to indicate the example image area 85. Is updated.
  • three of the four example images displayed with “ ⁇ ” have an example image having a comparison value equal to or less than the threshold value “50”, that is, the threshold value. It is the image for illustration which cleared. Further, one of the “x” displayed is an example image having a comparison value larger than the threshold value “50”, that is, an example image that has not cleared the threshold value.
  • FIG. 8B illustrates the case where three example images that have cleared the threshold and one example image that has not cleared the threshold are displayed.
  • the display number of images may be the same as the display number of example images that have not cleared the threshold.
  • FIG. 8 shows the case where both the example image with the threshold cleared and the example image with the threshold cleared are displayed. However, only the example image with the threshold cleared, or the example with the threshold not cleared. Only the image for use may be displayed. In addition, after displaying a predetermined number of example images, it is possible to display whether each example image has cleared the threshold value “ ⁇ ” or whether the threshold value has not been cleared “x”.
  • the example image to be exemplified in the image area 85b is not included in the example image information 15b, the example image to be exemplified by combining a plurality of example images included in the example image information 15b. Is displayed on the image area 85b.
  • FIG. 9 is a diagram illustrating a modification of the fine adjustment screen.
  • 9A shows a fine adjustment area 91 corresponding to each discrimination item
  • FIG. 9B shows a fine adjustment screen 95 including a plurality of fine adjustment areas 91. Yes.
  • the fine adjustment area 91 corresponding to each determination item includes an up button 92a for accepting an operation for increasing the threshold value, a down button 92b for accepting an operation for reducing the threshold value, and the latest threshold value. Is displayed and an example image area 94 in which an example image is displayed.
  • the example image area 94 includes a notification area 94a in which “ ⁇ ” or “x” is displayed, and an image area 94b in which an example image extracted from the example image information 15b is displayed. A plurality of sets are displayed.
  • FIG. 9A shows a case where the exemplary image area 94 further includes a slider bar 94c.
  • the example image information 15b does not include the example image to be displayed in the image area 94b, the example image to be exemplified by combining a plurality of example images included in the example image information 15b. Is displayed on the image area 94b.
  • the fine adjustment screen 95 includes a denomination area 96 indicating denominations, and a fine adjustment area 91 (see FIG. 9A) corresponding to each discrimination item. , And a composition area 97 and a confirm button 98 that is pressed to confirm the threshold value.
  • the synthesis area 97 is an area for displaying a synthesized image obtained by synthesizing the example image extracted with the latest threshold value for each discrimination item. For example, a case where an example image having comparison values of +10, +5, ⁇ 5, and ⁇ 10 with respect to the latest threshold is extracted for each determination item will be described. In this case, the exemplary images having the comparison value of +10 and the exemplary images having the comparison value of +5 are synthesized with respect to all the discrimination items.
  • FIG. 9 shows the fine adjustment screen 95 in which the fine adjustment areas 91 respectively corresponding to the discrimination items are arranged in the vertical and horizontal directions
  • the configuration of the screen is not limited to this. Therefore, hereinafter, a modified example of the threshold setting screen will be described.
  • FIG. 10 is a diagram showing a modification of the threshold setting screen.
  • symbol as FIG. 9 is attached
  • the threshold setting screen 200 includes a threshold display area 93 in which the latest threshold is displayed and an image area 94 b in which an exemplary image extracted from the exemplary image information 15 b is displayed for each discrimination item. Is displayed.
  • the threshold setting screen 200 is provided with a slider bar used for changing the threshold.
  • the threshold is increased, and when the slider 99 is moved in the direction 99b, the threshold is decreased.
  • the image area 94b an exemplary image corresponding to the changed threshold value is displayed. 9 may be provided on the threshold setting screen 200.
  • the example image information 15b does not include the example image to be displayed in the image area 94b, the example image to be exemplified by combining a plurality of example images included in the example image information 15b. Is displayed on the image area 94b.
  • FIG. 11 is a flowchart illustrating the processing procedure of the learning process.
  • the learning unit 14a obtains the distribution of correct bills (see FIG. 5) from the identification unit 11 (step S101), and also distributes the distribution of each lossy ticket level (see FIG. 5) from the identification unit 11. Obtain (step S102).
  • the learning unit 14a calculates a reference threshold for each loss ticket level (step S103), and determines whether there is a loss ticket level without a mother set, that is, whether there is an insufficient loss ticket level. (Step S104).
  • step S104 when there is a deficient ticket level (step S104, Yes), the reference threshold value of the corresponding level is estimated from the other banknote level (step S105). If the determination condition in step S104 is not satisfied (step S104, No), the process proceeds to step S106 without performing the process procedure in step S105.
  • the learning unit 14a registers the reference threshold calculated in step S103 and the reference threshold estimated in step S105 in the reference threshold information 15a (step S106), and ends the process.
  • FIG. 12 is a flowchart showing the processing procedure of the threshold adjustment processing.
  • the example unit 14b searches the example image information 15b using the reference threshold value (step S202).
  • the example unit 14b displays the extracted example image on the display unit 12 (step S203).
  • the threshold adjustment unit 14c determines whether or not a confirmation operation for the threshold has been accepted (step S204). If a confirmation operation has been accepted (Yes in step S204), the latest threshold is adjusted to the adjustment threshold information 15c. (Step S208), and the process ends.
  • step S204 when the confirmation operation has not been received (step S204, No), it is determined whether or not a threshold change operation has been received (step S205).
  • step S205 Yes
  • the example unit 14b searches the example image information 15b using the changed threshold value (step S206).
  • step S207 After the extracted exemplary image is displayed on the display unit 12 (step S207), the processes after step S204 are repeated. Even when the determination condition of step S205 is not satisfied (step S205, No), the processing after step S204 is repeated.
  • a desired example image is generated by combining a plurality of example images included in the example image information 15b. Good.
  • the storage unit stores image data obtained by capturing paper sheets, the input unit accepts a setting operation for a threshold value prepared for each discrimination item, and the example unit accepts it.
  • the example of the result of damage determination when the threshold value after the setting operation is used is determined based on the image data extracted using the threshold value after the setting operation. Therefore, it is possible to easily and appropriately set the parameter for determining the damage.
  • the reference threshold value and the example image are learned via the identification unit included in the paper sheet identification device.
  • the reference threshold value and the example image generated by another device are identified as the paper sheet. It is good also as making it memorize
  • the example mentioned above demonstrated the case where the actual image of a defective ticket was switched and illustrated in connection with the change operation of a threshold value
  • the image for example, illustration
  • the grade of a damaged ticket is switched and illustrated. It is good as well.
  • a plurality of illustrations corresponding to the threshold value may be stored in advance, and when the threshold value is changed, an illustration corresponding to the changed threshold value may be exemplified.
  • the paper sheet discriminating apparatus and the paper sheet discriminating method according to the present invention are useful when it is desired to easily and appropriately set the parameters for discriminating damage, and in particular, the proficiency level of the operator is Even if it is low, it is suitable for the case where it is desired to easily and appropriately set the parameter for damage determination.

Landscapes

  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Inspection Of Paper Currency And Valuable Securities (AREA)

Abstract

Disclosed is a paper sheet identification device configured so that a storage unit stores image data of paper sheets, an input unit accepts setting operation with respect to a threshold prepared for each identification item, and an exemplification unit exemplifies, on the basis of image data extracted using the threshold after the setting operation, the result of normal-or-damaged identification in the case of using the threshold after the accepted setting operation. Moreover, the paper sheet identification device is configured so that the exemplification unit extracts, on the basis of the threshold after the setting operation, image data for exemplification of identification as normal and/or image data for exemplification of identification as damaged.

Description

紙葉類判別装置および紙葉類判別方法Paper sheet discriminating apparatus and paper sheet discriminating method
　本発明は、紙葉類の正損を判別する紙葉類判別装置および紙葉類判別方法に関し、特に、正損判別用パラメータの設定を容易かつ適切に行わせることができる紙葉類判別装置および紙葉類判別方法に関する。 The present invention relates to a paper sheet discriminating apparatus and a paper sheet discriminating method for discriminating whether a paper sheet is correct or not, and in particular, a paper sheet discriminating apparatus capable of easily and appropriately setting a parameter for discriminating damage. And a paper sheet discrimination method.
　従来、紙幣や商品券といった紙葉類の正損を判別する紙葉類判別装置が知られている。たとえば、特許文献１には、損券の程度を示す損券レベルを多段階に設けたうえで、判別対象となる紙葉類が該当する損券レベルを判別して損券レベル毎の枚数分布を表示する紙葉類判別装置が開示されている。 Conventionally, a paper sheet discriminating apparatus that discriminates whether paper sheets such as banknotes and gift certificates are correct or not is known. For example, in Japanese Patent Laid-Open No. 2004-228620, a non-performing ticket level indicating the degree of a non-performing ticket is provided in multiple stages, and the number of non-performing slips is determined by determining the pertinent slip level corresponding to the paper sheet to be determined. Is disclosed.
　特許文献１の紙葉類判別装置を用いた場合、紙葉類判別装置のオペレータは、紙葉類の判別処理の完了後に損券レベル毎の枚数分布を目視する。そして、どのような状態の紙葉類が損券と判別されたかを確認し、正損判別用パラメータ（以下、単に「パラメータ」と記載する）の再設定によって所望する正損判別の程度を調整することになる。 When the paper sheet discriminating apparatus disclosed in Patent Document 1 is used, the operator of the paper sheet discriminating apparatus visually observes the sheet number distribution for each loss ticket level after completing the paper sheet discriminating process. Then, check the state of the paper sheet that was determined to be a non-performing bill, and adjust the degree of desired damage determination by resetting the damage determination parameter (hereinafter simply referred to as “parameter”). Will do.
　また、特許文献２には、パラメータの変更画面において、パラメータに対応する判別項目をあらわす模式的な画像を表示する紙葉類判別装置が開示されている。たとえば、特許文献２の紙葉類判別装置では、「角折れ」についてのパラメータを設定する場合には、角折れ状態を示す画像を表示する。 Further, Patent Document 2 discloses a paper sheet discriminating apparatus that displays a schematic image representing a discrimination item corresponding to a parameter on a parameter change screen. For example, in the paper sheet discriminating apparatus disclosed in Patent Document 2, when a parameter for “corner break” is set, an image indicating a corner break state is displayed.
特開平５－２８２５１６号公報Japanese Patent Laid-Open No. 5-282516 特開２００９－２９４９３３号公報JP 2009-294933 A
　しかしながら、特許文献１の紙葉類判別装置は、多数の紙葉類についての判別処理が完了した後にはじめてパラメータを再設定するための指針を提示するので、パラメータ調整に要する時間が長引くとともに、煩雑な調整作業をオペレータへ強いてしまうという問題があった。このため、パラメータの調整を適切に行わせることが難しいという問題もあった。 However, since the paper sheet discriminating apparatus of Patent Document 1 presents a guideline for resetting parameters only after discrimination processing for a large number of paper sheets is completed, the time required for parameter adjustment is prolonged and complicated. There was a problem that forced adjustment work was forced on the operator. For this reason, there is a problem that it is difficult to appropriately adjust the parameters.
　また、特許文献２の紙葉類識別装置は、調整対象のパラメータが、どの判別項目に該当するかをオペレータへ通知しているにすぎず、パラメータ調整後にどのような判別結果が得られるのかを通知するものではない。 In addition, the paper sheet identification device of Patent Document 2 merely notifies the operator of which determination item the parameter to be adjusted corresponds to, and what determination result is obtained after parameter adjustment. Not a notification.
　これらのことから、正損判別用パラメータの設定を容易かつ適切に行わせることができる紙葉類判別装置あるいは紙葉類判別方法をいかにして実現するかが大きな課題となっている。 For these reasons, it is a big problem how to realize a paper sheet discrimination apparatus or a paper sheet discrimination method capable of easily and appropriately setting the parameters for damage discrimination.
　本発明は、上述した従来技術の課題を解決するためになされたものであり、正損判別用パラメータの設定を容易かつ適切に行わせることができる紙葉類判別装置および紙葉類判別方法を提供することを目的とする。 The present invention has been made to solve the above-described problems of the prior art, and provides a paper sheet discriminating apparatus and a paper sheet discriminating method capable of easily and appropriately setting a parameter for discriminating damage. The purpose is to provide.
　上述した課題を解決し、目的を達成するために、本発明は、紙葉類の正損を判別する紙葉類判別装置であって、画像データを記憶する記憶手段と、判別項目ごとに用意された閾値に対する設定操作を受け付ける受付手段と、前記受付手段によって受け付けられた設定操作後の前記閾値を用いた場合における正損判別結果の例示を、当該閾値を用いて抽出した前記画像データに基づいて行う例示手段とを備えたことを特徴とする。 In order to solve the above-described problems and achieve the object, the present invention is a paper sheet discriminating apparatus for discriminating whether a paper sheet is normal or not, and includes a storage means for storing image data and a discrimination item. Based on the image data extracted by using the threshold value, an example of the result of determining whether the threshold value after the setting operation received by the receiving unit and the threshold value after the setting operation received by the receiving unit is used. And an example means for performing the above.
　また、本発明は、上記の発明において、前記例示手段は、前記設定操作後の前記閾値に基づき、正判別の例示となる前記画像データおよび／または損判別の例示となる前記画像データを前記記憶手段から抽出することを特徴とする。 Further, according to the present invention, in the above invention, the example means stores the image data that is an example of correct determination and / or the image data that is an example of loss determination based on the threshold value after the setting operation. It is characterized by extracting from the means.
　また、本発明は、上記の発明において、前記記憶手段は、紙葉類を撮像した撮像データを前記画像データとして記憶することを特徴とする。 Further, the present invention is characterized in that, in the above-mentioned invention, the storage means stores imaging data obtained by imaging a paper sheet as the image data.
　また、本発明は、上記の発明において、前記記憶手段は、前記閾値の比較対象となる比較値が前記判別項目ごとにそれぞれ関連付けられた前記画像データを記憶し、前記例示手段は、前記閾値以上の前記比較値と関連付けられた前記画像データおよび／または前記閾値未満の前記比較値と関連付けられた前記画像データを前記判別項目ごとにそれぞれ抽出し、抽出した前記画像データとともに当該画像データが正判別の例示であるか損判別の例示であるかの区別を表示することを特徴とする。 Further, the present invention is the above invention, wherein the storage means stores the image data in which a comparison value to be compared with the threshold value is associated with each of the determination items, and the example means has the threshold value equal to or higher than the threshold value. The image data associated with the comparison value and / or the image data associated with the comparison value less than the threshold value are extracted for each of the determination items, and the image data together with the extracted image data is positively determined. It is characterized by displaying the distinction of whether it is an example of the above or an example of loss determination.
　また、本発明は、上記の発明において、前記閾値を用いて紙葉類の正損を判別する判別手段をさらに備え、前記判別手段は、前記画像データを入力として受け付け、当該画像データについて前記判別項目ごとに算出した前記比較値を当該画像データと関連付けて前記記憶手段へ記憶させることを特徴とする。 In the invention described above, the present invention further includes a determination unit that determines whether the paper sheet is normal or not by using the threshold value. The determination unit receives the image data as an input, and the determination is performed on the image data. The comparison value calculated for each item is stored in the storage unit in association with the image data.
　また、本発明は、上記の発明において、前記例示手段は、特定の前記判別項目について例示すべき前記比較値を有する前記画像データがない場合に、当該判別項目についてそれぞれ異なる前記比較値を有する前記画像データから例示すべき前記画像データを生成することを特徴とする。 Further, in the present invention, in the above invention, when there is no image data having the comparison value to be exemplified for the specific discrimination item, the example means has the comparison value different for the discrimination item. The image data to be exemplified is generated from the image data.
　また、本発明は、上記の発明において、前記例示手段は、それぞれ異なる前記判別項目ごとに抽出された前記画像データを合成した合成データを例示することを特徴とする。 Further, the present invention is characterized in that, in the above invention, the example means exemplifies composite data obtained by combining the image data extracted for each of the different discrimination items.
　また、本発明は、紙葉類の正損を判別する紙葉類判別方法であって、画像データを記憶部へ記憶させる記憶工程と、判別項目ごとに用意された閾値に対する設定操作を受け付ける受付工程と、前記受付工程によって受け付けられた設定操作後の前記閾値を用いた場合における正損判別結果の例示を、当該閾値を用いて抽出した前記画像データに基づいて行う例示工程とを含んだことを特徴とする。 The present invention is also a paper sheet discrimination method for discriminating whether a paper sheet is normal or not, and a storage process for storing image data in a storage unit and a reception operation for accepting a setting operation for a threshold prepared for each discrimination item. And an example step of performing an example of the result of damage determination when the threshold value after the setting operation received by the receiving step is used based on the image data extracted using the threshold value. It is characterized by.
　本発明によれば、画像データを記憶し、判別項目ごとに用意された閾値に対する設定操作を受け付け、受け付けられた設定操作後の閾値を用いた場合における正損判別結果の例示を、設定操作後の閾値を用いて抽出した画像データに基づいて行うこととしたので、正損判別結果の例示を、設定操作後の閾値に対応する画像データで行うことによって、正損判別用パラメータの設定を容易かつ適切に行わせることができるという効果を奏する。 According to the present invention, image data is stored, a setting operation with respect to a threshold value prepared for each determination item is received, and an example of a fitness determination result when the received threshold value after the setting operation is used is illustrated after the setting operation. Since the determination is made based on the image data extracted using the threshold value, it is easy to set the damage determination parameter by exemplifying the damage determination result with the image data corresponding to the threshold value after the setting operation. And there is an effect that it can be appropriately performed.
　また、本発明によれば、設定操作後の閾値に基づき、正判別の例示となる画像データおよび／または損判別の例示となる画像データを抽出することとしたので、どのような状態の損券が正判別され、どのような状態の損券が損判別されるのかを、オペレータへ的確に伝えることができるという効果を奏する。 Further, according to the present invention, image data that is an example of correct discrimination and / or image data that is an example of loss discrimination are extracted based on the threshold value after the setting operation. Is positively determined, and it is possible to accurately tell to the operator what state of the non-performing slip is determined.
　また、本発明によれば、紙葉類を撮像した撮像データを画像データとして記憶することとしたので、どのような状態の損券が正判別され、どのような状態の損券が損判別されるのかを、実画像の例示によって、オペレータへより的確に伝えることができるという効果を奏する。 In addition, according to the present invention, the imaging data obtained by imaging the paper sheet is stored as the image data, so that the state of the damaged ticket is determined correctly, and the state of the damaged ticket is determined as lost. This is advantageous in that it can be conveyed to the operator more accurately by exemplifying the actual image.
　また、本発明によれば、閾値の比較対象となる比較値が判別項目ごとにそれぞれ関連付けられた画像データを記憶することとしたうえで、閾値以上の比較値と関連付けられた画像データおよび／または閾値未満の比較値と関連付けられた画像データを判別項目ごとにそれぞれ抽出し、抽出した画像データとともにこの画像データが正判別の例示であるか損判別の例示であるかの区別を表示することとしたので、閾値変更によって得られる判別結果を的確かつ速やかにオペレータへ伝えることができるという効果を奏する。 According to the present invention, the image data associated with the comparison value equal to or greater than the threshold value and / or the image data associated with the comparison value to be compared with the threshold value are stored for each discrimination item. Extracting image data associated with a comparison value less than the threshold value for each discrimination item, and displaying with the extracted image data whether the image data is an example of correct discrimination or an example of loss discrimination As a result, the determination result obtained by changing the threshold value can be accurately and promptly transmitted to the operator.
　また、本発明によれば、閾値を用いて紙葉類の正損を判別することとしたうえで、正損の判別において画像データを入力として受け付け、受け付けた画像データについて判別項目ごとに算出した比較値を画像データと関連付けて記憶させることとしたので、正損判別に用いるデバイスの個体差がある場合であっても、適切な例示画像を抽出することができるという効果を奏する。 In addition, according to the present invention, after determining whether the paper sheet is normal or not using the threshold, the image data is received as input in the determination of the normality, and the received image data is calculated for each determination item. Since the comparison value is stored in association with the image data, there is an effect that it is possible to extract an appropriate example image even when there is an individual difference in devices used for damage determination.
　また、本発明によれば、特定の判別項目について例示すべき比較値を有する画像データがない場合に、この判別項目についてそれぞれ異なる比較値を有する画像データから例示すべき画像データを生成することとしたので、複数の判別項目を組み合わせた例示画像を提供することによって、正損判別用パラメータの設定をより容易かつ適切に行わせることができるという効果を奏する。 According to the present invention, when there is no image data having a comparison value to be exemplified for a specific discrimination item, image data to be exemplified is generated from image data having a different comparison value for the discrimination item. Therefore, by providing an exemplary image in which a plurality of discrimination items are combined, there is an effect that it is possible to more easily and appropriately set the parameter for damage discrimination.
　また、本発明によれば、それぞれ異なる判別項目ごとに抽出された画像データを合成した合成データを例示することとしたので、表示すべき実画像データが存在しない場合であっても、例示画像を提供することができるという効果を奏する。 In addition, according to the present invention, since the combined data obtained by combining the image data extracted for each different discrimination item is illustrated, the exemplary image is displayed even when there is no actual image data to be displayed. There is an effect that it can be provided.
図１は、本発明に係る紙葉類判別手法の概要を示す図である。FIG. 1 is a diagram showing an outline of a paper sheet discrimination method according to the present invention. 図２は、本実施例に係る紙葉類判別装置の構成を示すブロック図である。FIG. 2 is a block diagram illustrating the configuration of the paper sheet discriminating apparatus according to the present embodiment. 図３は、紙幣処理装置の内部構成を示す図である。FIG. 3 is a diagram showing an internal configuration of the banknote handling apparatus. 図４は、紙幣処理装置の動作例を示す図である。FIG. 4 is a diagram illustrating an operation example of the banknote handling apparatus. 図５は、学習処理の概要を示す図である。FIG. 5 is a diagram showing an outline of the learning process. 図６は、基準閾値情報および例示用画像情報の一例を示す図である。FIG. 6 is a diagram illustrating an example of the reference threshold information and the exemplary image information. 図７は、選択画面の一例を示す図である。FIG. 7 is a diagram illustrating an example of the selection screen. 図８は、微調整画面の一例を示す図である。FIG. 8 is a diagram illustrating an example of a fine adjustment screen. 図９は、微調整画面の変形例を示す図である。FIG. 9 is a diagram illustrating a modification of the fine adjustment screen. 図１０は、閾値設定画面の変形例を示す図である。FIG. 10 is a diagram illustrating a modification of the threshold setting screen. 図１１は、学習処理の処理手順を示すフローチャートである。FIG. 11 is a flowchart illustrating the processing procedure of the learning process. 図１２は、閾値調整処理の処理手順を示すフローチャートである。FIG. 12 is a flowchart showing the processing procedure of the threshold adjustment processing.
　以下に、添付図面を参照して、本発明に係る紙葉類判別装置および紙葉類判別方法の好適な実施例を詳細に説明する。なお、以下では、本発明に係る紙葉類判別手法の概要について図１を用いて説明した後に、本発明に係る紙葉類判別手法を適用した紙葉類判別装置についての実施例を説明することとする。 Hereinafter, preferred embodiments of a paper sheet discriminating apparatus and a paper sheet discriminating method according to the present invention will be described in detail with reference to the accompanying drawings. In the following, the outline of the paper sheet discrimination method according to the present invention will be described with reference to FIG. 1, and then an embodiment of the paper sheet discrimination apparatus to which the paper sheet discrimination method according to the present invention is applied will be described. I will do it.
　図１は、本発明に係る紙葉類判別手法の概要を示す図である。同図に示すように、本発明に係る紙葉類判別手法は、あらかじめ取得した損券の実画像をデータベース化しておき、正損判別用の閾値を設定する際に、変更後の閾値に対応する実画像をデータベースから抽出して例示する点に主たる特徴がある。 FIG. 1 is a diagram showing an outline of a paper sheet discrimination method according to the present invention. As shown in the figure, the paper sheet discriminating method according to the present invention creates a database of the actual images of the acquired slips in advance, and corresponds to the changed threshold when setting the threshold for discriminating damage. The main feature is that an actual image to be extracted is illustrated from a database.
　このように、損券の実画像を例示することで、閾値設定作業を行うオペレータに対してどのような状態の損券が損券と判別（以下、「損判別」と記載する）され、どのような状態の損券が正券と判別（以下、「正判別」と記載する）されるのかを周知させることができる。これにより、閾値変更後にテスト用の損券を実際に判別させる作業を、何度も繰り返し行う必要がなくなるので、閾値設定作業を簡易かつ短時間で終了させることができる。 In this way, by demonstrating the actual image of a non-performing ticket, what type of non-performing ticket is identified as a non-performing ticket (hereinafter referred to as “determination”) for the operator who performs the threshold setting operation. It is possible to make it known whether a non-performing ticket in such a state is discriminated as a genuine note (hereinafter referred to as “correct discrimination”). Thus, it is not necessary to repeat the work of actually determining the test slip after changing the threshold value, so that the threshold setting work can be completed easily and in a short time.
　具体的には、本発明に係る紙葉類判別手法では、正券群（所定枚数の正券）、損券のレベルごとに用意された各損券群（同図では、レベルＡおよびレベルＢ）を用いた学習を行うことで基準閾値ＤＢ（データベース）を生成する（同図の（Ａ－１）参照）。ここで、「レベル」とは、正損判別に用いる複数の判別項目ごとに、閾値の比較対象となる比較値を複数段階に区分けした各数値範囲のことを指す。 Specifically, in the paper sheet discriminating method according to the present invention, the correct note group (predetermined number of correct notes), and each non-issued ticket group prepared for each level of non-issued ticket (in the figure, level A and level B). ) Is used to generate a reference threshold value DB (database) (see (A-1) in the figure). Here, “level” refers to each numerical range obtained by dividing a comparison value to be compared with a threshold value into a plurality of stages for each of a plurality of discrimination items used for discrimination of fitness.
　また、「基準閾値」とは、初期値として設定される閾値のことを指し、各判別項目に対応する基準閾値をすべての判別項目について組み合わせたセットを、複数セット有するものとする。そして、どのセットをデフォルト値として用いるかをオペレータに選択させる。 Also, the “reference threshold value” refers to a threshold value set as an initial value, and has a plurality of sets in which the reference threshold value corresponding to each discrimination item is combined for all discrimination items. Then, the operator selects which set is used as a default value.
　なお、どのセットをデフォルト値として用いるかを装置の出荷時にあらかじめ設定しておいてもよい。この場合、装置の出荷先となる国や地域ごとに、デフォルト値として用いるセットを設定しておけばよい。 Note that which set is used as a default value may be set in advance at the time of shipment of the apparatus. In this case, a set used as a default value may be set for each country or region to which the device is shipped.
　また、本発明に係る紙葉類判別手法では、各損券群に含まれる損券を実際に撮像することで例示用画像ＤＢ（データベース）を生成する（同図の（Ａ－２）参照）。ここで、例示用画像ＤＢには、各判別項目の比較値と、画像データとを関連付けた情報が格納される。 Further, in the paper sheet discrimination method according to the present invention, an example image DB (database) is generated by actually imaging the slips included in each banknote group (see (A-2) in the figure). . Here, the example image DB stores information in which the comparison value of each discrimination item is associated with the image data.
　そして、本発明に係る紙葉類判別手法では、閾値の調整を行う閾値調整画面に対して基準閾値ＤＢから選択された基準閾値をデフォルト値として表示する（同図の（Ｂ）参照）。また、閾値の変更操作と連動して「閾値をクリアした例示用画像」および「閾値をクリアしなかった例示用画像」を、例示用画像ＤＢから抽出する処理を繰り返す（同図の（Ｃ）参照）。 In the paper sheet discriminating method according to the present invention, the reference threshold value selected from the reference threshold value DB is displayed as a default value on the threshold value adjustment screen for adjusting the threshold value (see (B) in the figure). Further, in conjunction with the threshold changing operation, the process of extracting the “exemplary image that has cleared the threshold” and the “exemplary image that has not cleared the threshold” from the exemplary image DB is repeated ((C) in the figure). reference).
　また、本発明に係る紙葉類判別手法では、「閾値をクリアした例示用画像」とともに閾値をクリアした旨（同図の「○」参照）を、「閾値をクリアしなかった例示用画像」とともに閾値をクリアしなかった旨（同図の「×」参照）を、それぞれ表示する。 Further, in the paper sheet discrimination method according to the present invention, the fact that the threshold has been cleared together with the “example image that has cleared the threshold” (see “◯” in the same figure) indicates that the example image has not cleared the threshold. At the same time, the fact that the threshold value has not been cleared (see “X” in the figure) is displayed.
　このように、本発明に係る紙葉類判別手法では、閾値の変更などの閾値設定操作を受け付けた場合に、最新の閾値で損券と判別される損券の実画像や、最新の閾値で正券と判別される損券の実画像を表示することとした。なお、例示する実画像の数については任意に設定することができる。 As described above, in the paper sheet discriminating method according to the present invention, when a threshold setting operation such as a threshold change is accepted, an actual image of a slip that is discriminated as a slip by the latest threshold or the latest threshold. An actual image of a non-performing ticket that is identified as a genuine ticket is displayed. Note that the number of actual images illustrated can be arbitrarily set.
　したがって、本発明に係る紙葉類判別手法によれば、正損判別用パラメータの設定を容易かつ適切に行わせることができることができる。なお、図１では、閾値をクリアした例示用画像および閾値をクリアしなかった例示用画像の双方を例示する場合について示したが、閾値をクリアした例示用画像のみ、あるいは、閾値をクリアしなかった例示用画像のみを表示することとしてもよい。 Therefore, according to the paper sheet discriminating method according to the present invention, it is possible to easily and appropriately set the parameters for discriminating damage. In addition, in FIG. 1, although the case where both the example image which cleared the threshold value and the example image which did not clear the threshold value was illustrated was shown, only the example image which cleared the threshold value, or the threshold value is not cleared Only the example image may be displayed.
　以下では、図１を用いて説明した紙葉類判別手法を適用した紙葉類判別装置の実施例について説明する。なお、以下に示す実施例では、正損判別機能を有する紙葉類判別装置について説明するが、閾値設定の機能のみを有する装置（たとえば、閾値設定装置）に対して本発明を適用することとしてもよい。 Hereinafter, an embodiment of a paper sheet discriminating apparatus to which the paper sheet discriminating method described with reference to FIG. 1 is applied will be described. In the following embodiment, a paper sheet discriminating apparatus having a function for discriminating damage is described. However, the present invention is applied to an apparatus having only a threshold setting function (for example, a threshold setting apparatus). Also good.
　また、以下に示す実施例では、閾値の変更操作と連動し、損券の「実画像」を切り替えて例示する場合について説明するが、損券の程度を示す画像（たとえば、「イラスト」などの画像データ）を、切り替えて例示することとしてもよい。 Further, in the embodiment described below, a case where the “real image” of the damaged ticket is switched and illustrated in conjunction with the threshold changing operation will be described, but an image showing the degree of the damaged ticket (for example, “illustration” or the like) (Image data) may be exemplified by switching.
　図２は、本実施例に係る紙葉類判別装置１０の構成を示すブロック図である。同図に示すように、紙葉類判別装置１０は、識別部１１と、表示部１２と、入力部１３と、制御部１４と、記憶部１５とを備えている。 FIG. 2 is a block diagram showing the configuration of the paper sheet discriminating apparatus 10 according to the present embodiment. As shown in the figure, the paper sheet discriminating apparatus 10 includes an identification unit 11, a display unit 12, an input unit 13, a control unit 14, and a storage unit 15.
　また、制御部１４は、学習部１４ａと、例示部１４ｂと、閾値調整部１４ｃと、識別データ取得部１４ｄと、正損判別部１４ｅとをさらに備えている。そして、記憶部１５は、基準閾値情報１５ａと、例示用画像情報１５ｂと、調整閾値情報１５ｃとを記憶する。 The control unit 14 further includes a learning unit 14a, an example unit 14b, a threshold adjustment unit 14c, an identification data acquisition unit 14d, and a fitness determination unit 14e. The storage unit 15 stores reference threshold information 15a, exemplary image information 15b, and adjustment threshold information 15c.
　識別部１１は、画像センサや磁気センサを用いて紙葉類の種別や、紙葉類の正損を判別する処理を行うデバイスである。ここで、識別部１１は、紙葉類判別装置１０が学習モードで動作する場合には、判別項目ごとに取得した比較値を、紙葉類を撮像した画像データと関連付けたうえで、制御部１４の学習部１４ａへ通知する。また、識別部１１は、紙葉類判別装置１０が判別モードで動作する場合には、判別項目ごとに取得した比較値を、制御部１４の識別データ取得部１４ｄへ通知する。 The identification unit 11 is a device that performs processing for discriminating the type of paper sheet and the correctness of the paper sheet using an image sensor and a magnetic sensor. Here, when the paper sheet discriminating apparatus 10 operates in the learning mode, the identification unit 11 associates the comparison value acquired for each discrimination item with the image data obtained by imaging the paper sheet, and then the control unit 14 learning units 14a. Further, when the paper sheet discriminating apparatus 10 operates in the discrimination mode, the identification unit 11 notifies the identification data acquisition unit 14d of the control unit 14 of the comparison value acquired for each discrimination item.
　表示部１２は、液晶ディスプレイなどの表示デバイスであり、制御部１４の例示部１４ｂによって生成された閾値設定用の画面などを表示する。また、入力部１３は、テンキーなどの入力デバイスであり、オペレータによる操作内容を制御部１４の閾値調整部１４ｃへ通知する。なお、本実施例では、表示部１２と入力部１３とを別々に設けた場合について説明するが、両者の機能を兼ね備えたデバイス、たとえば、タッチパネルディスプレイを用いることとしてもよい。 The display unit 12 is a display device such as a liquid crystal display, and displays a threshold setting screen or the like generated by the example unit 14b of the control unit 14. The input unit 13 is an input device such as a numeric keypad, and notifies the threshold adjustment unit 14c of the control unit 14 of the operation content by the operator. In this embodiment, the case where the display unit 12 and the input unit 13 are provided separately will be described. However, a device having both functions, for example, a touch panel display may be used.
　ここで、図２に示した紙葉類判別装置１０の一例である紙幣処理装置１００について図３および図４を用いて説明しておく。図３は、紙幣処理装置１００の内部構成を示す図であり、図４は、紙幣処理装置１００の動作例を示す図である。 Here, the banknote processing apparatus 100 which is an example of the paper sheet discriminating apparatus 10 shown in FIG. 2 will be described with reference to FIG. 3 and FIG. FIG. 3 is a diagram illustrating an internal configuration of the banknote handling apparatus 100, and FIG. 4 is a diagram illustrating an operation example of the banknote handling apparatus 100.
　図３に示すように、紙幣処理装置１００は、入金部１０１と、第１出金部１０２ａと、第２出金部１０２ｂと、一時保留部１０２ｃと、搬送部１０３と、識別部１０４と、操作表示部１０５と、コントローラ１０６とを備えている。 As shown in FIG. 3, the banknote handling apparatus 100 includes a depositing unit 101, a first dispensing unit 102a, a second dispensing unit 102b, a temporary holding unit 102c, a transport unit 103, an identification unit 104, An operation display unit 105 and a controller 106 are provided.
　また、紙幣処理装置１００は、精査カセット１０７と、スタッカ１０８ａと、スタッカ１０８ｂと、スタッカ１０８ｃと、スタッカ１０８ｄと、スタッカ１０８ｅと、回収カセット１０９とを備えている。 Further, the banknote handling apparatus 100 includes a scrutiny cassette 107, a stacker 108a, a stacker 108b, a stacker 108c, a stacker 108d, a stacker 108e, and a collection cassette 109.
　なお、識別部１０４は図２の識別部１１に、操作表示部１０５は同じく表示部１２および入力部１３に、コントローラ１０６は同じく制御部１４および記憶部１５に、それぞれ対応している。また、コントローラ１０６は、他装置との通信インタフェースや、レシート印字などを行うプリンタを含むものとする。 The identification unit 104 corresponds to the identification unit 11 in FIG. 2, the operation display unit 105 corresponds to the display unit 12 and the input unit 13, and the controller 106 corresponds to the control unit 14 and the storage unit 15. The controller 106 includes a communication interface with other apparatuses and a printer that performs receipt printing.
　入金部１０１は、ユーザやオペレータが投入した紙幣を受け付けるデバイスであり、入金部１０１へ入金された紙幣は、搬送部１０３へ送り出される。第１出金部１０２ａおよび第２出金部１０２ｂは、搬送部１０３から受け取った紙幣を出金するデバイスである。また、一時保留部１０２ｃは、入金部１０１から入金された紙幣や、各スタッカ（１０８ａ～１０８ｅ）などから繰り出された紙幣を、一時的に保持するデバイスである。 The depositing unit 101 is a device that accepts bills inserted by a user or an operator, and the bills deposited into the depositing unit 101 are sent out to the transport unit 103. The first withdrawal unit 102 a and the second withdrawal unit 102 b are devices that withdraw banknotes received from the transport unit 103. The temporary storage unit 102c is a device that temporarily holds banknotes deposited from the depositing unit 101 and banknotes fed out from the stackers (108a to 108e).
　搬送部１０３は、入金部１０１から入金された紙幣を、識別部１０４経由で、一時保留部１０２ｃや各スタッカ（１０８ａ～１０８ｅ）へ搬送するデバイスである。また、この搬送部１０３は、各スタッカ（１０８ａ～１０８ｅ）などから繰り出された紙幣を、第１出金部１０２ａや第２出金部１０２ｂへ搬送する。 The transport unit 103 is a device that transports the banknote deposited from the deposit unit 101 to the temporary storage unit 102c and each stacker (108a to 108e) via the identification unit 104. Further, the transport unit 103 transports banknotes fed from the stackers (108a to 108e) to the first withdrawal unit 102a and the second withdrawal unit 102b.
　スタッカ１０８ａ、スタッカ１０８ｂ、スタッカ１０８ｃ、スタッカ１０８ｄおよびスタッカ１０８ｅは、たとえば、金種ごとにそれぞれ用意された格納部であり、搬送部１０３から受け取った紙幣を蓄積するとともに、繰出指示を受け付けた場合には、指示された枚数の紙幣を搬送部１０３へ繰り出す。 The stacker 108a, the stacker 108b, the stacker 108c, the stacker 108d, and the stacker 108e are storage units prepared for respective denominations, for example, when the banknotes received from the transport unit 103 are accumulated and a feeding instruction is received. Feeds the designated number of banknotes to the transport unit 103.
　精査カセット１０７は、各スタッカ（１０８ａ～１０８ｅ）に蓄積された紙幣を再識別する場合などに用いられるカセットである。たとえば、スタッカ１０８ｄで紙幣詰まりなどの障害が発生して復旧した場合に、スタッカ１０８ｄからすべての紙幣を繰り出し、識別部１０４による再識別を経て精査カセット１０７へ格納する。 The scrutiny cassette 107 is a cassette used when re-identifying banknotes accumulated in the stackers (108a to 108e). For example, when a failure such as banknote jam occurs in the stacker 108d and is recovered, all banknotes are fed out from the stacker 108d and stored in the scrutinizing cassette 107 through re-identification by the identification unit 104.
　そして、精査カセット１０７へ格納された再識別後の紙幣は、搬送部１０３経由でスタッカ１０８ｄへ戻されることになる。また、回収カセット１０９は、紙幣処理装置１００から紙幣を回収する場合に用いられる着脱式の格納部である。 The re-identified banknotes stored in the scrutiny cassette 107 are returned to the stacker 108d via the transport unit 103. The collection cassette 109 is a detachable storage unit used when collecting banknotes from the banknote processing apparatus 100.
　次に、図３に示した紙幣処理装置１００の動作例について図４を用いて説明する。なお、図４の（Ａ）には、入金時の動作例を、図４の（Ｂ）には、出金時の動作例を、それぞれ示している。 Next, an operation example of the banknote handling apparatus 100 shown in FIG. 3 will be described with reference to FIG. 4A shows an operation example at the time of depositing, and FIG. 4B shows an operation example at the time of withdrawal.
　まず、入金時の動作例について説明する。図４の（Ａ）に示すように、ユーザやオペレータによって紙幣が入金部１０１へ入金されると（同図の（Ａ－１）参照）、搬送部１０３は、入金された紙幣を識別部１０４へ搬送し、識別部１０４は、紙幣の識別処理を実行する（同図の（Ａ－２）参照）。 First, an example of operation when depositing will be described. As shown in FIG. 4A, when a banknote is deposited into the depositing unit 101 by a user or an operator (see (A-1) in FIG. 4), the transport unit 103 identifies the deposited banknote as the identifying unit 104. The identification unit 104 executes bill recognition processing (see (A-2) in the figure).
　そして、識別部１０４によって正券と識別された紙幣は、一時保留部１０２ｃへいったん格納される（同図の（Ａ－３ａ））。一方、識別部１０４によって正券以外（たとえば、偽券や損券）と識別された紙幣は、第２出金部１０２ｂへ返却される（同図の（Ａ－３ｂ）参照）。 Then, the banknotes identified as correct bills by the identifying unit 104 are temporarily stored in the temporary holding unit 102c ((A-3a) in the figure). On the other hand, banknotes identified by the identifying unit 104 as other than genuine (for example, counterfeit or damaged) are returned to the second dispensing unit 102b (see (A-3b) in the figure).
　また、一時保留部１０２ｃへいったん格納された正券は、操作表示部１０５が確定操作を受け付けた場合に、該当するスタッカ（１０８ａ～１０８ｅのいずれか）へそれぞれ収納される（同図の（Ａ－４）参照）。そして、正券が収納されたスタッカ（１０８ａ～１０８ｅのいずれか）の在高の更新（この場合は加算）が行われる（同図の（Ａ－５）参照）。 Further, the genuine bills once stored in the temporary holding unit 102c are respectively stored in the corresponding stackers (any one of 108a to 108e) when the operation display unit 105 accepts the confirmation operation ((A -4)). Then, the stock amount (in this case, addition) of the stacker (any one of 108a to 108e) storing the correct ticket is updated (see (A-5) in the figure).
　なお、図４の（Ａ）では、一時保留部１０２ｃを用いた場合の動作を例示したが、一時保留部１０２ｃを用いないこととしてもよい。この場合、識別部１０４によって正券と識別された紙幣は、一時保留部１０２ｃを経由することなく、該当するスタッカ（１０８ａ～１０８ｅのいずれか）へそれぞれ収納される。一方、識別部１０４によって正券以外（たとえば、偽券や損券）と識別された紙幣は、第２出金部１０２ｂへ返却される。そして、正券が収納されたスタッカ（１０８ａ～１０８ｅのいずれか）の在高の更新が行われることになる。 4A illustrates the operation when the temporary holding unit 102c is used, the temporary holding unit 102c may not be used. In this case, the banknotes identified as correct bills by the identifying unit 104 are respectively stored in the corresponding stackers (any one of 108a to 108e) without going through the temporary holding unit 102c. On the other hand, the banknotes identified by the identifying unit 104 as other than the regular bill (for example, a fake ticket or a non-performing bill) are returned to the second dispensing unit 102b. Then, the stock amount of the stacker (any one of 108a to 108e) in which the correct ticket is stored is updated.
　次に、出金時の動作例について説明する。図４の（Ｂ）に示すように、操作表示部１０５が出金操作を受け付けると（同図の（Ｂ－１）参照）、該当するスタッカ（１０８ａ～１０８ｅ）から所定枚数の紙幣が繰り出される（同図の（Ｂ－２）参照）。そして、繰り出された紙幣は、搬送部１０３経由で第１出金部１０２ａへ出金される（同図の（Ｂ－３）参照）。 Next, an example of operation at the time of withdrawal will be described. As shown in FIG. 4B, when the operation display unit 105 accepts a withdrawal operation (see (B-1) in FIG. 4), a predetermined number of banknotes are paid out from the corresponding stackers (108a to 108e). (See (B-2) in the figure). The fed banknotes are withdrawn to the first withdrawal unit 102a via the transport unit 103 (see (B-3) in the figure).
　なお、スタッカ（１０８ａ～１０８ｅ）から繰り出された紙幣を識別部１０４で再識別することとしてもよい。この場合、識別部１０４で異常が発見された紙幣は、一時保留部１０２ｃへいったん格納され、所定のタイミングで、繰り出し元のスタッカ（１０８ａ～１０８ｅ）へ戻される。 Note that the bills fed out of the stackers (108a to 108e) may be re-identified by the identification unit 104. In this case, the banknote in which the abnormality is found in the identification unit 104 is temporarily stored in the temporary storage unit 102c, and returned to the feeding stacker (108a to 108e) at a predetermined timing.
　つづいて、第１出金部１０２ａから紙幣が取り出されると（同図の（Ｂ－４）参照）、繰り出し元のスタッカ（１０８ａ～１０８ｅ）の在高の更新（この場合は減算）が行われる（同図の（Ｂ－５）参照）。 Subsequently, when the banknote is taken out from the first withdrawal unit 102a (see (B-4) in the same figure), the stock amount of the stacker (108a to 108e) as the feeding source is updated (subtraction in this case). (See (B-5) in the figure).
　図２の説明に戻り、紙葉類判別装置１０についての説明をつづける。制御部１４は、紙葉類判別装置１０の全体制御を行う制御部である。学習部１４ａは、紙葉類判別装置１０が学習モードで動作している場合に、識別部１１から、判別項目ごとに取得した比較値と、紙葉類を撮像した画像データとを受け取り、記憶部１５の基準閾値情報１５ａおよび例示用画像情報１５ｂを生成する処理を行う処理部である。 Returning to the explanation of FIG. 2, the explanation of the paper sheet discriminating apparatus 10 will be continued. The control unit 14 is a control unit that performs overall control of the paper sheet discriminating apparatus 10. When the paper sheet discriminating apparatus 10 is operating in the learning mode, the learning unit 14a receives and stores the comparison value acquired for each discrimination item and the image data obtained by imaging the paper sheet from the discrimination unit 11. It is a processing unit that performs processing for generating the reference threshold information 15a and the exemplary image information 15b of the unit 15.
　具体的には、この学習部１４ａは、正券群（所定枚数の正券）、損券のレベルごとに用意された各損券群（同図では、レベルＡおよびレベルＢ）を用いた学習を行うことで、基準閾値を含んだ基準閾値情報１５ａを生成する。ここで、基準閾値としては、たとえば、強、中、弱の３種類を生成する。 Specifically, the learning unit 14a uses the correct ticket group (predetermined number of correct tickets), and learning using each of the damaged ticket groups (level A and level B in the figure) prepared for each level of the damaged ticket. To generate the reference threshold value information 15a including the reference threshold value. Here, for example, three types of strong, medium, and weak are generated as the reference threshold.
　また、学習部１４ａは、各損券を撮像した各画像データと、判別項目ごとに識別部１１で取得された各比較値とを関連付けた例示用画像情報１５ｂを損券ごとに生成する。ここで、各画像データに対応する比較値が欠落している場合には、この学習部１４ａは、欠落した比較値以外の比較値にそれぞれ対応する画像データを合成することで、欠落した比較値に対応する画像データを生成する処理を行う。 Further, the learning unit 14a generates, for each banknote, exemplary image information 15b in which each image data obtained by capturing each banknote is associated with each comparison value acquired by the identification unit 11 for each discrimination item. Here, when the comparison value corresponding to each image data is missing, the learning unit 14a synthesizes the image data corresponding to the comparison values other than the missing comparison value to thereby obtain the missing comparison value. The process which produces | generates the image data corresponding to is performed.
　たとえば、閾値の調整が１きざみで可能である場合において、比較値が「５」の画像データと、比較値が「７」の画像データとが存在し、比較値が「６」の画像データが欠落しているとする。この場合、学習部１４ａは、比較値が「５」および「７」の画像データを合成して比較値が「６」の画像データを生成する。 For example, when the threshold can be adjusted in increments of 1, there is image data with a comparison value of “5” and image data with a comparison value of “7”, and image data with a comparison value of “6”. Suppose that it is missing. In this case, the learning unit 14a combines the image data with the comparison values “5” and “7” to generate image data with the comparison value “6”.
　なお、欠落した比較値に対応する画像データを学習部１４ａがあらかじめ生成するのではなく、画像データを表示する際、たとえば、閾値の変更操作を受け付けた後に生成することとしてもよい。この場合、例示すべき画像データが存在しないならば、例示すべき画像データを、他の比較値に対応する複数の画像データを合成することによって生成すればよい。 Note that the image data corresponding to the missing comparison value is not generated in advance by the learning unit 14a, but may be generated, for example, after receiving a threshold change operation when displaying the image data. In this case, if there is no image data to be exemplified, the image data to be exemplified may be generated by combining a plurality of image data corresponding to other comparison values.
　ここで、学習部１４ａによって行われる学習処理の概要について図５を、学習部１４ａによって生成される基準閾値情報１５ａおよび例示用画像情報１５ｂの例について図６を、それぞれ用いて説明しておく。 Here, the outline of the learning process performed by the learning unit 14a will be described with reference to FIG. 5, and examples of the reference threshold information 15a and the example image information 15b generated by the learning unit 14a will be described with reference to FIG.
　まず、学習処理の概要について説明する。図５は、学習処理の概要を示す図である。なお、同図の（Ａ）には、学習処理の基本例を、同図の（Ｂ）には、所定の損券レベルの母集合がない場合の閾値推定の例を、それぞれ示している。また、同図に示した各グラフの横軸は、判別項目ごとの比較値を、縦軸は分布度数（たとえば、紙葉類の枚数）を、それぞれあらわしている。 First, the outline of the learning process will be described. FIG. 5 is a diagram showing an outline of the learning process. In addition, (A) in the figure shows a basic example of the learning process, and (B) in the figure shows an example of threshold estimation in the case where there is no set of a predetermined banknote level. Further, the horizontal axis of each graph shown in the figure represents the comparison value for each discrimination item, and the vertical axis represents the distribution frequency (for example, the number of paper sheets).
　図５の（Ａ）に示すように、学習部１４ａは、所定枚数からなる正券群について識別部１１が識別した識別結果に基づき、正券分布を生成する（同図の破線参照）。ここで、正券分布は、たとえば、正規分布となる。 As shown in FIG. 5A, the learning unit 14a generates a correct distribution based on the identification result identified by the identifying unit 11 with respect to a predetermined number of correct tickets (see the broken line in FIG. 5). Here, the correct note distribution is, for example, a normal distribution.
　また、学習部１４ａは、損券レベルごとに所定枚数からなる損券群それぞれについて識別部１１が識別した識別結果に基づき、各損券分布を生成する。ここで、同図には、損券レベルＡの損券群、損券レベルＢの損券群、損券レベルＣの損券群をそれぞれ用いた場合について示している。なお、損券の程度は、レベルＣが最もひどく、レベルＡが最も正券に近いものとする。 Further, the learning unit 14a generates each banknote distribution based on the identification result identified by the identification unit 11 for each banknote group consisting of a predetermined number for each banknote level. Here, the figure shows a case where a non-ticket group at a non-slip level A, a non-slip group at a non-slip level B and a non-slip group at a non-slip level C are used. It is assumed that the level of the loss ticket is the worst in level C and the closest to the correct ticket in level A.
　このように、学習部１４ａは、複数の損券レベルについて損券分布をそれぞれ生成する。そして、学習部１４ａは、損券レベルＡ用の閾値Ａ、損券レベルＢ用の閾値Ｂ、損券レベルＣ用の閾値Ｃを決定する。なお、同図では、各損券分布の頂点付近に各閾値を決定した場合について示している。そして、決定された閾値Ａ、閾値Ｂおよび閾値Ｃは、上記した基準閾値として用いられることになる。 In this way, the learning unit 14a generates a loss distribution for each of a plurality of loss ticket levels. Then, the learning unit 14a determines a threshold A for the slipped ticket level A, a threshold B for the slipped ticket level B, and a threshold C for the slipped ticket level C. In the figure, a case is shown in which each threshold value is determined near the apex of each banknote distribution. Then, the determined threshold A, threshold B, and threshold C are used as the above-described reference threshold.
　たとえば、損券レベルＡに対応する閾値Ａを基準閾値として用いた場合には、上記した判別モードにおいて、すべての紙幣のうち３０％程度が損券と判別され、閾値Ｂを基準閾値として用いた場合には１０％程度が損券と判別される。また、閾値Ｃを基準閾値として用いた場合には３％程度が損券と判別される。 For example, when the threshold value A corresponding to the loss ticket level A is used as the reference threshold value, about 30% of all banknotes are determined as the loss tickets in the above-described determination mode, and the threshold value B is used as the reference threshold value. In some cases, about 10% is determined to be a non-performing ticket. Further, when the threshold value C is used as the reference threshold value, about 3% is determined to be a bad ticket.
　このように、閾値Ａ、閾値Ｂおよび閾値Ｃのうちどの閾値を基準閾値として選択するかによって、紙葉類判別装置１０における正損判別の傾向を変更することができる。 Thus, depending on which threshold value among the threshold value A, threshold value B, and threshold value C is selected as the reference threshold value, the tendency of the damage discrimination in the paper sheet discriminating apparatus 10 can be changed.
　ところで、図５の（Ａ）では、損券レベルＡの損券群、損券レベルＢの損券群、損券レベルＣの損券群をそれぞれ用いる場合について説明したが、所定の損券レベルの損券群を用意できない場合も想定される。このため、学習部１４ａは、欠落した損券レベルに対応する閾値を、他の損券レベルに対応する損券分布に基づいて推定する処理を行う。 By the way, in FIG. 5 (A), the case where the non-ticket group of the non-slip level A, the non-slip group of the non-slip level B, and the non-slip group of the non-slip level C are described, respectively. It may be assumed that a group of non-performing tickets cannot be prepared. For this reason, the learning part 14a performs the process which estimates the threshold value corresponding to the missing banknote level based on the banknote distribution corresponding to another banknote level.
　たとえば、学習部１４ａは、図５の（Ｂ）に示したように、損券レベルＢの損券群が存在しない場合には、損券レベルＡに対応する閾値Ａおよび損券レベルＢに対応する閾値Ｃに基づき、損券レベルＢの損券分布に対応する閾値Ｂを推定する（同図の（Ｂ－１）参照）。 For example, as shown in FIG. 5B, the learning unit 14a corresponds to the threshold A corresponding to the loss ticket level A and the loss ticket level B when there is no loss ticket group of the loss ticket level B. Based on the threshold value C to be estimated, the threshold value B corresponding to the loss distribution at the loss ticket level B is estimated (see (B-1) in the figure).
　具体的には、損券レベルＡの範囲が３０～５０、損券レベルＢの範囲が５０～７０、損券レベルＣの範囲が７０～９０である場合、学習部１４ａは、閾値Ａと閾値Ｃとの平均値を閾値Ｂとする。なお、ここでは、損券レベルＡおよびＣに対応する母集合が存在し、損券レベルＢに対応する母集合が存在しない場合に、損券レベルＢに対応する閾値を損券レベルＡおよびＣの分布を用いた内挿補間によって推定する場合について示した。 Specifically, when the range of the non-slip ticket level A is 30 to 50, the range of the non-slip ticket level B is 50 to 70, and the range of the non-slip ticket level C is 70 to 90, the learning unit 14a An average value with C is defined as a threshold value B. Here, when there is a mother set corresponding to the non-ticket level A and C, and there is no mother set corresponding to the non-ticket level B, the threshold corresponding to the non-ticket level B is set to the non-ticket level A and C. The case of estimation by interpolation using the distribution of is shown.
　しかしながら、これに限らず、欠落した損券レベルに対応する閾値を外挿補間によって推定することとしてもよい。たとえば、損券レベルＡおよびＢに対応する母集合が存在し、損券レベルＣに対応する母集合が存在しない場合には、損券レベルＡおよびＢの分布を用いた外挿補間によって損券レベルＣに対応する閾値を推定することとすればよい。 However, the present invention is not limited to this, and the threshold value corresponding to the missing banknote level may be estimated by extrapolation. For example, when there is a population set corresponding to the loss ticket levels A and B and there is no population set corresponding to the loss ticket level C, the loss tickets are obtained by extrapolation using the distribution of the loss ticket levels A and B. The threshold value corresponding to level C may be estimated.
　次に、基準閾値情報１５ａおよび例示用画像情報１５ｂの例について説明する。図６は、基準閾値情報１５ａおよび例示用画像情報１５ｂの一例を示す図である。なお、同図の（Ａ）には基準閾値情報１５ａの例を、同図の（Ｂ）には例示用画像情報１５ｂの例を、それぞれ示している。また、同図には、判別対象となる紙葉類が紙幣である場合の例を示している。 Next, examples of the reference threshold information 15a and the example image information 15b will be described. FIG. 6 is a diagram illustrating an example of the reference threshold information 15a and the exemplary image information 15b. Note that (A) in the figure shows an example of the reference threshold information 15a, and (B) in the figure shows an example of the illustrative image information 15b. Moreover, in the same figure, the example in case the paper sheets used as discrimination | determination object is a banknote is shown.
　図６の（Ａ）に示すように、基準閾値情報１５ａは、「金種」項目と、「損券レベル」項目と、「基準閾値」項目とを含んでいる。ここで、「基準閾値」項目は、正損判別における判別項目ごとに区分されており、図６に示した場合では、「テープ券」項目と、「破れ」項目と、「汚れ（部分）」項目と、「汚れ（全体）」項目と、「落書き」項目と、「かすれ」項目とをさらに含んでいる。 As shown in FIG. 6A, the reference threshold value information 15a includes a “denomination” item, a “defect ticket level” item, and a “reference threshold” item. Here, the “reference threshold value” item is classified for each discrimination item in the damage discrimination. In the case shown in FIG. 6, the “tape ticket” item, the “tear” item, and the “dirt (part)”. It further includes an item, a “dirt (whole)” item, a “graffiti” item, and a “blur” item.
　また、基準閾値情報１５ａには、１つの金種について、複数の損券レベルそれぞれの基準閾値が格納される。なお、同図には、１つの金種について、損券レベルＡ、損券レベルＢおよび損券レベルＣにそれぞれ対応する基準閾値が格納される場合を例示している。 Also, the reference threshold information 15a stores a reference threshold for each of a plurality of banknote levels for one denomination. In addition, the same figure has illustrated the case where the reference threshold value corresponding to each of the banknote level A, banknote level B, and banknote level C is stored about one denomination.
　たとえば、金種αの損券レベルＡについては、基準閾値がそれぞれ、「テープ券」項目については「５０」、「破れ」項目については「６０」、「汚れ（部分）」項目については「７０」、「汚れ（全体）」項目については「１００」、「落書き」項目については「１５０」、「かすれ」項目については「２００」である。なお、各基準閾値は、上記した学習部１４ａによって設定される。 For example, with respect to the loss ticket level A of the denomination α, the reference threshold values are “50” for the “tape ticket” item, “60” for the “tear” item, and “70” for the “dirt (part)” item. ”And“ dirt (whole) ”items are“ 100 ”,“ graffiti ”items are“ 150 ”, and“ blur ”items are“ 200 ”. Each reference threshold is set by the learning unit 14a.
　このように、基準閾値情報１５ａは、金種ごと、損券レベルごとに、複数の判別項目にそれぞれ対応する基準閾値が格納された情報である。なお、金種αについて損券レベルＢがデフォルト設定された場合には、各基準閾値として、「６０」、「７０」、「８０」、「１１０」、「１６０」および「２１０」がそれぞれ用いられることになる。 As described above, the reference threshold value information 15a is information in which reference threshold values corresponding to a plurality of discrimination items are stored for each denomination and each non-performing ticket level. If the default level B is set for the denomination α, “60”, “70”, “80”, “110”, “160”, and “210” are used as the respective reference threshold values. Will be.
　また、図６の（Ｂ）に示すように、例示用画像情報１５ｂは、「画像ＩＤ」項目と、「比較値」項目とを含んでいる。ここで、「画像ＩＤ」項目には、画像データを一意に識別する数字や記号の組み合わせを用いることができるが、紙幣から読み取った記番号（紙幣に印字されている識別用のコード）を用いることとしてもよい。また、「比較値」項目は、正損判別における判別項目ごとに区分けされている。なお、区分けの内容は、図６の（Ａ）と同様であるので、ここでの説明は省略する。 Also, as shown in FIG. 6B, the exemplary image information 15b includes an “image ID” item and a “comparison value” item. Here, a combination of numbers and symbols that uniquely identify image data can be used for the “image ID” item, but a serial number (an identification code printed on the banknote) read from the banknote is used. It is good as well. In addition, the “comparison value” item is classified for each discrimination item in the fitness discrimination. Note that the contents of the classification are the same as those in FIG. 6A, and a description thereof is omitted here.
　たとえば、「画像ＩＤ」項目が「１１１１１」の画像データに対応する比較値は、「１０」、「２０」、「３０」、「４０」、「５０」および「６０」である。また、「画像ＩＤ」項目が「２２２２２」の画像データに対応する比較値は、「２０」、「３０」、「４０」、「５０」、「６０」および「７０」である。なお、各比較値は、上記した学習部１４ａによって設定される。 For example, the comparison values corresponding to the image data whose “image ID” item is “11111” are “10”, “20”, “30”, “40”, “50”, and “60”. Further, the comparison values corresponding to the image data whose “image ID” item is “22222” are “20”, “30”, “40”, “50”, “60”, and “70”. Each comparison value is set by the learning unit 14a.
　また、図６の（Ｂ）では、例示用画像情報１５ｂに画像データそのものが含まれない場合について示しているが、例示用画像情報１５ｂに画像データそのものを含めることとしてもよい。 6B shows a case where the image data itself is not included in the exemplary image information 15b, the image data itself may be included in the exemplary image information 15b.
　図２の説明に戻り、制御部１４についての説明をつづける。例示部１４ｂは、入力部１３経由で閾値調整指示を受けた場合に、調整後の閾値を用いて例示用画像情報１５ｂから閾値をクリアする画像データおよび閾値をクリアしない画像データを所定数ずつ抽出する処理を行う処理部である。 Returning to the description of FIG. 2, the description of the control unit 14 will be continued. When receiving the threshold adjustment instruction via the input unit 13, the example unit 14b extracts a predetermined number of image data that clears the threshold and image data that does not clear the threshold from the example image information 15b using the adjusted threshold. It is a processing part which performs processing to perform.
　また、この例示部１４ｂは、抽出した画像データを含んだ画面を表示部１２に対して表示させる処理を併せて行う。なお、例示部１４ｂは、閾値調整用の画面における閾値の初期値として基準閾値情報１５ａの基準閾値を用いる。また、例示部１４ｂが生成する画面の例については、図７～図１０を用いて後述する。 The example unit 14b also performs a process of displaying a screen including the extracted image data on the display unit 12. The example unit 14b uses the reference threshold value of the reference threshold value information 15a as the initial value of the threshold value on the threshold adjustment screen. An example of the screen generated by the example unit 14b will be described later with reference to FIGS.
　閾値調整部１４ｃは、入力部１３から閾値変更などの指示を受けた場合に、調整後の閾値を調整閾値情報１５ｃとして記憶部１５へ記憶させる処理を行う処理部である。なお、調整閾値情報１５ｃの構成は、図６の（Ａ）に示した基準閾値情報１５ａと同様である。また、調整閾値情報１５ｃが更新されるたびに、例示部１４ｂによる例示用画像情報１５ｂの再抽出処理および閾値調整用の画面の更新処理が行われる。 The threshold adjustment unit 14c is a processing unit that performs processing for storing the adjusted threshold value in the storage unit 15 as the adjustment threshold information 15c when an instruction to change the threshold value is received from the input unit 13. The configuration of the adjustment threshold information 15c is the same as the reference threshold information 15a shown in FIG. Each time the adjustment threshold information 15c is updated, re-extraction processing of the exemplary image information 15b and update processing of the threshold adjustment screen are performed by the exemplary unit 14b.
　識別データ取得部１４ｄは、紙葉類判別装置１０が判別モードで動作する場合に、判別項目ごとに取得された比較値を識別部１１から受け取るとともに、受け取った比較値を正損判別部１４ｅへ渡す処理を行う処理部である。また、正損判別部１４ｅは、識別データ取得部１４ｄから受け取った比較値を、最終的に確定された調整閾値情報１５ｃとそれぞれ比較することで、紙葉類の正損判別を行う処理部である。 When the paper sheet discriminating apparatus 10 operates in the discrimination mode, the identification data acquisition unit 14d receives the comparison value acquired for each discrimination item from the discrimination unit 11 and the received comparison value to the fitness discrimination unit 14e. It is a processing unit that performs a passing process. In addition, the damage determination unit 14e is a processing unit that determines the damage of the paper sheet by comparing the comparison value received from the identification data acquisition unit 14d with the finally determined adjustment threshold information 15c. is there.
　記憶部１５は、ハードディスクドライブや不揮発性メモリといった記憶デバイスであり、基準閾値情報１５ａ、例示用画像情報１５ｂおよび調整閾値情報１５ｃを記憶する。なお、基準閾値情報１５ａ、例示用画像情報１５ｂおよび調整閾値情報１５ｃの内容については、図６などを用いて既に説明したので、ここでの説明は省略する。 The storage unit 15 is a storage device such as a hard disk drive or a non-volatile memory, and stores reference threshold information 15a, exemplary image information 15b, and adjustment threshold information 15c. The contents of the reference threshold information 15a, the exemplary image information 15b, and the adjustment threshold information 15c have already been described with reference to FIG.
　次に、例示部１４ｂが生成する画面の例について図７～図１０を用いて説明する。図７は、選択画面の一例を示す図である。なお、同図の（Ａ）には、金種選択画面７１を、同図の（Ｂ）には、項目選択画面７２を、それぞれ示している。 Next, examples of screens generated by the example unit 14b will be described with reference to FIGS. FIG. 7 is a diagram illustrating an example of the selection screen. Note that (A) in the figure shows a denomination selection screen 71 and (B) in the figure shows an item selection screen 72, respectively.
　図７の（Ａ）に示すように、金種選択画面７１は、金種ごとに用意された金種選択ボタン７１ａを有している。たとえば、「金種α」に対応する金種選択ボタン７１ａが選択された場合には、図７の（Ｂ）に示した項目選択画面７２が表示されることになる。 7A, the denomination selection screen 71 has a denomination selection button 71a prepared for each denomination. For example, when the denomination selection button 71a corresponding to “denomination α” is selected, the item selection screen 72 shown in FIG. 7B is displayed.
　図７の（Ｂ）に示したように、項目選択画面７２は、正損判別の判別項目に対応する判別項目選択ボタン７２ａと、判別項目における基準閾値が初期値として表示される閾値エリア７２ｂとの組を、判別項目数分有している。また、項目選択画面７２には、微調整の対象となる金種（同図では「金種α」）が併せて表示される。 As shown in FIG. 7B, the item selection screen 72 includes a determination item selection button 72a corresponding to a determination item for fitness determination, and a threshold area 72b in which a reference threshold value in the determination item is displayed as an initial value. There are as many sets as there are discriminating items. The item selection screen 72 also displays a denomination (“denomination α” in the figure) to be finely adjusted.
　そして、「テープ券」に対応する判別項目選択ボタン７２ａが選択された場合には、図８に示す微調整画面８１が表示されることになる。 When the determination item selection button 72a corresponding to “tape ticket” is selected, a fine adjustment screen 81 shown in FIG. 8 is displayed.
　図８は、微調整画面８１の一例を示す図である。なお、同図の（Ａ）には、最初に表示される微調整画面８１の例を、同図の（Ｂ）には、閾値変更操作受け付け後の微調整画面８１の例を、それぞれ示している。 FIG. 8 is a diagram illustrating an example of the fine adjustment screen 81. Note that (A) in the figure shows an example of the fine adjustment screen 81 displayed first, and (B) in the figure shows an example of the fine adjustment screen 81 after acceptance of the threshold change operation. Yes.
　図８の（Ａ）に示すように、微調整画面８１は、閾値の微調整対象となる金種および判別項目が表示されるインフォメーションエリア８２と、閾値を大きくする操作を受け付けるアップボタン８３ａと、閾値を小さくする操作を受け付けるダウンボタン８３ｂと、最新の閾値が表示される閾値表示エリア８４と、例示画像が表示される例示画像エリア８５と、閾値を確定する場合に押下される確定ボタン８６とを有している。 As shown in FIG. 8A, the fine adjustment screen 81 includes an information area 82 in which denominations and discrimination items that are targets for fine adjustment of the threshold are displayed, an up button 83a that receives an operation for increasing the threshold, A down button 83b for accepting an operation for reducing the threshold value, a threshold value display area 84 for displaying the latest threshold value, an example image area 85 for displaying an example image, and a confirm button 86 for pressing the threshold value have.
　ここで、例示画像エリア８５には、「○」または「×」が表示される通知エリア８５ａと、例示用画像情報１５ｂから抽出された例示用画像が表示される画像エリア８５ｂとの組が、複数組表示される。 Here, in the exemplary image area 85, a set of a notification area 85a in which “◯” or “×” is displayed and an image area 85b in which the exemplary image extracted from the exemplary image information 15b is displayed. Multiple sets are displayed.
　たとえば、図８の（Ａ）に示した場合には、４つの例示用画像のうち、「○」が表示された２つが、閾値「５０」以下の比較値をもつ例示用画像、すなわち、閾値をクリアした例示用画像である。また、「×」が表示された２つが、閾値「５０」よりも大きい比較値をもつ例示用画像、すなわち、閾値をクリアしなかった例示用画像である。 For example, in the case illustrated in FIG. 8A, two of the four example images in which “◯” is displayed have an example image having a comparison value equal to or less than the threshold value “50”, that is, the threshold value. It is the image for illustration which cleared. Two of the “x” displayed are example images having comparison values larger than the threshold value “50”, that is, example images that have not cleared the threshold value.
　そして、各例示用画像の比較値は、同図の上から順に、たとえば、「３５」、「４５」、「５５」、「６５」である。なお、比較値が「４５」の例示用画像が存在しない場合には、例示用画像情報１５ｂに含まれる例示用画像のうち、比較値「４５」に近い比較値をもつ複数の例示用画像を合成することとすればよい。また、例示用画像を例示用画像情報１５ｂから抽出する際に、どのような比較値を有する例示用画像を抽出するかについての条件は、適宜、定めることができる。 Then, the comparison values of the image for illustration are, for example, “35”, “45”, “55”, “65” in order from the top of the figure. When there is no example image with the comparison value “45”, among the example images included in the example image information 15b, a plurality of example images having comparison values close to the comparison value “45” are selected. What is necessary is just to synthesize | combine. Moreover, when extracting the example image from the example image information 15b, the conditions for extracting the example image having what kind of comparison value can be determined as appropriate.
　ここで、図８の（Ｂ）に示したように、たとえば、アップボタン８３ａが操作され、閾値が「５５」へ変更された場合には、あらたな閾値「５５」を用いて例示画像エリア８５が更新される。 Here, as shown in FIG. 8B, for example, when the up button 83a is operated and the threshold value is changed to “55”, the new image threshold value “55” is used to indicate the example image area 85. Is updated.
　たとえば、図８の（Ｂ）に示した場合には、４つの例示用画像のうち、「○」が表示された３つが、閾値「５０」以下の比較値をもつ例示用画像、すなわち、閾値をクリアした例示用画像である。また、「×」が表示された１つが、閾値「５０」よりも大きい比較値をもつ例示用画像、すなわち、閾値をクリアしなかった例示用画像である。 For example, in the case shown in FIG. 8B, three of the four example images displayed with “◯” have an example image having a comparison value equal to or less than the threshold value “50”, that is, the threshold value. It is the image for illustration which cleared. Further, one of the “x” displayed is an example image having a comparison value larger than the threshold value “50”, that is, an example image that has not cleared the threshold value.
　このように、閾値の変更操作を受け付けると、微調整画面８１には、あらたな閾値に対応する例示用画像が再表示されるので、オペレータに対して閾値変更後の判別状況を正確に伝達することができる。 As described above, when the threshold value changing operation is accepted, an example image corresponding to the new threshold value is redisplayed on the fine adjustment screen 81, so that the determination status after the threshold value change is accurately transmitted to the operator. be able to.
　なお、図８の（Ｂ）には、閾値をクリアした例示用画像を３つ、閾値をクリアしなかった例示用画像を１つ、それぞれ表示した場合を例示したが、閾値をクリアした例示用画像の表示数と、閾値をクリアしなかった例示用画像の表示数とを同数にしてもよい。 FIG. 8B illustrates the case where three example images that have cleared the threshold and one example image that has not cleared the threshold are displayed. The display number of images may be the same as the display number of example images that have not cleared the threshold.
　また、図８では、閾値をクリアした例示用画像および閾値をクリアした例示用画像の双方を表示する場合について示したが、閾値をクリアした例示用画像のみ、あるいは、閾値をクリアしなかった例示用画像のみを表示することとしてもよい。また、所定数の例示画像を表示させたうえで、各例示画像が閾値をクリアしたか「○」、閾値をクリアしなかったか「×」を、それぞれ表示させることとしてもよい。 FIG. 8 shows the case where both the example image with the threshold cleared and the example image with the threshold cleared are displayed. However, only the example image with the threshold cleared, or the example with the threshold not cleared. Only the image for use may be displayed. In addition, after displaying a predetermined number of example images, it is possible to display whether each example image has cleared the threshold value “◯” or whether the threshold value has not been cleared “x”.
　なお、画像エリア８５ｂへ例示すべき例示用画像が例示用画像情報１５ｂに含まれない場合には、例示用画像情報１５ｂに含まれる複数の例示用画像を合成することによって例示すべき例示用画像を生成したうえで、画像エリア８５ｂへ表示する。 In addition, when the example image to be exemplified in the image area 85b is not included in the example image information 15b, the example image to be exemplified by combining a plurality of example images included in the example image information 15b. Is displayed on the image area 85b.
　次に、微調整画面の変形例について図９を用いて説明する。図９は、微調整画面の変形例を示す図である。なお、図９の（Ａ）には、判別項目にそれぞれ対応する微調整エリア９１を、図９の（Ｂ）には、微調整エリア９１を複数個含んだ微調整画面９５を、それぞれ示している。 Next, a modification of the fine adjustment screen will be described with reference to FIG. FIG. 9 is a diagram illustrating a modification of the fine adjustment screen. 9A shows a fine adjustment area 91 corresponding to each discrimination item, and FIG. 9B shows a fine adjustment screen 95 including a plurality of fine adjustment areas 91. Yes.
　図９の（Ａ）に示すように、判別項目にそれぞれ対応する微調整エリア９１は、閾値を大きくする操作を受け付けるアップボタン９２ａと、閾値を小さくする操作を受け付けるダウンボタン９２ｂと、最新の閾値が表示される閾値表示エリア９３と、例示画像が表示される例示画像エリア９４とを有している。 As shown in FIG. 9A, the fine adjustment area 91 corresponding to each determination item includes an up button 92a for accepting an operation for increasing the threshold value, a down button 92b for accepting an operation for reducing the threshold value, and the latest threshold value. Is displayed and an example image area 94 in which an example image is displayed.
　ここで、例示画像エリア９４は、図８と同様に、「○」または「×」が表示される通知エリア９４ａと、例示用画像情報１５ｂから抽出された例示用画像が表示される画像エリア９４ｂとの組が、複数組表示される。なお、図９の（Ａ）には、例示画像エリア９４がスライダーバー９４ｃをさらに有している場合を示している。 Here, as in FIG. 8, the example image area 94 includes a notification area 94a in which “◯” or “x” is displayed, and an image area 94b in which an example image extracted from the example image information 15b is displayed. A plurality of sets are displayed. FIG. 9A shows a case where the exemplary image area 94 further includes a slider bar 94c.
　すなわち、例示画像エリア９４の表示面積を小さくしたい場合には、たとえば、「○」と「×」とを１つずつ表示する。そして、スライダーバー９４ｃが操作された場合に、あらかじめ抽出しておいた例示画像を例示画像エリア９４へ表示させる。 That is, when it is desired to reduce the display area of the exemplary image area 94, for example, “◯” and “X” are displayed one by one. Then, when the slider bar 94c is operated, the example image extracted in advance is displayed in the example image area 94.
　なお、画像エリア９４ｂへ表示すべき例示用画像が例示用画像情報１５ｂに含まれない場合には、例示用画像情報１５ｂに含まれる複数の例示用画像を合成することによって例示すべき例示用画像を生成したうえで画像エリア９４ｂへ表示する。 If the example image information 15b does not include the example image to be displayed in the image area 94b, the example image to be exemplified by combining a plurality of example images included in the example image information 15b. Is displayed on the image area 94b.
　また、図９の（Ｂ）に示したように、微調整画面９５は、金種を示す金種エリア９６と、判別項目にそれぞれ対応する微調整エリア９１（図９の（Ａ）参照）と、合成エリア９７と、閾値を確定する場合に押下される確定ボタン９８とを有している。 Further, as shown in FIG. 9B, the fine adjustment screen 95 includes a denomination area 96 indicating denominations, and a fine adjustment area 91 (see FIG. 9A) corresponding to each discrimination item. , And a composition area 97 and a confirm button 98 that is pressed to confirm the threshold value.
　ここで、合成エリア９７は、判別項目ごとに最新の閾値で抽出された例示用画像を合成した合成画像を表示するエリアである。たとえば、最新の閾値に対して、＋１０、＋５、－５および－１０の比較値をもつ例示用画像を、各判別項目について抽出した場合について説明する。この場合、すべての判別項目について＋１０の比較値をもつ例示用画像同士、＋５の比較値をもつ例示用画像同士を、それぞれ合成する。 Here, the synthesis area 97 is an area for displaying a synthesized image obtained by synthesizing the example image extracted with the latest threshold value for each discrimination item. For example, a case where an example image having comparison values of +10, +5, −5, and −10 with respect to the latest threshold is extracted for each determination item will be described. In this case, the exemplary images having the comparison value of +10 and the exemplary images having the comparison value of +5 are synthesized with respect to all the discrimination items.
　ところで、図９では、判別項目にそれぞれ対応する微調整エリア９１を縦横方向に並べた微調整画面９５を示したが、画面の構成は、これに限られるものではない。そこで、以下では、閾値設定画面の変形例について説明することとする。 Incidentally, although FIG. 9 shows the fine adjustment screen 95 in which the fine adjustment areas 91 respectively corresponding to the discrimination items are arranged in the vertical and horizontal directions, the configuration of the screen is not limited to this. Therefore, hereinafter, a modified example of the threshold setting screen will be described.
　図１０は、閾値設定画面の変形例を示す図である。なお、図９と同様の構成要素については図９と同一の符号を付している。図１０に示すように、閾値設定画面２００は、判別項目ごとに、最新の閾値が表示される閾値表示エリア９３と、例示用画像情報１５ｂから抽出された例示用画像が表示される画像エリア９４ｂとの組が表示される。 FIG. 10 is a diagram showing a modification of the threshold setting screen. In addition, the same code | symbol as FIG. 9 is attached | subjected about the component similar to FIG. As illustrated in FIG. 10, the threshold setting screen 200 includes a threshold display area 93 in which the latest threshold is displayed and an image area 94 b in which an exemplary image extracted from the exemplary image information 15 b is displayed for each discrimination item. Is displayed.
　また、閾値設定画面２００には、閾値変更に用いられるスライダーバーが設けられており、スライダー９９を、方向９９ａへ動かすと閾値が大きくなり、方向９９ｂへ動かすと閾値が小さくなる。そして、画像エリア９４ｂには、変更後の閾値に対応する例示用画像が表示される。なお、図９に示した合成エリア９７を、閾値設定画面２００に設けることとしてもよい。 Also, the threshold setting screen 200 is provided with a slider bar used for changing the threshold. When the slider 99 is moved in the direction 99a, the threshold is increased, and when the slider 99 is moved in the direction 99b, the threshold is decreased. In the image area 94b, an exemplary image corresponding to the changed threshold value is displayed. 9 may be provided on the threshold setting screen 200.
　なお、画像エリア９４ｂへ表示すべき例示用画像が例示用画像情報１５ｂに含まれない場合には、例示用画像情報１５ｂに含まれる複数の例示用画像を合成することによって例示すべき例示用画像を生成したうえで画像エリア９４ｂへ表示する。 If the example image information 15b does not include the example image to be displayed in the image area 94b, the example image to be exemplified by combining a plurality of example images included in the example image information 15b. Is displayed on the image area 94b.
　次に、学習部１４ａによって実行される学習処理の処理手順について図１１を用いて説明する。図１１は、学習処理の処理手順を示すフローチャートである。同図に示すように、学習部１４ａは、識別部１１から正券の分布（図５参照）を取得するとともに（ステップＳ１０１）、識別部１１から各損券レベルの分布（図５参照）を取得する（ステップＳ１０２）。 Next, the processing procedure of the learning process executed by the learning unit 14a will be described with reference to FIG. FIG. 11 is a flowchart illustrating the processing procedure of the learning process. As shown in the figure, the learning unit 14a obtains the distribution of correct bills (see FIG. 5) from the identification unit 11 (step S101), and also distributes the distribution of each lossy ticket level (see FIG. 5) from the identification unit 11. Obtain (step S102).
　つづいて、学習部１４ａは、各損券レベルの基準閾値を算出し（ステップＳ１０３）、母集合がない損券レベルがあるか否か、すなわち、不足の損券レベルがあるか否かを判定する（ステップＳ１０４）。 Subsequently, the learning unit 14a calculates a reference threshold for each loss ticket level (step S103), and determines whether there is a loss ticket level without a mother set, that is, whether there is an insufficient loss ticket level. (Step S104).
　そして、不足の損券レベルがある場合には（ステップＳ１０４，Ｙｅｓ）、他の損券レベルから該当レベルの基準閾値を推定する（ステップＳ１０５）。なお、ステップＳ１０４の判定条件を満たさなかった場合には（ステップＳ１０４，Ｎｏ）、ステップＳ１０５の処理手順を行うことなくステップＳ１０６の処理手順へ進む。 And when there is a deficient ticket level (step S104, Yes), the reference threshold value of the corresponding level is estimated from the other banknote level (step S105). If the determination condition in step S104 is not satisfied (step S104, No), the process proceeds to step S106 without performing the process procedure in step S105.
　そして、学習部１４ａは、ステップＳ１０３で算出した基準閾値ならびにステップＳ１０５で推定した基準閾値を、基準閾値情報１５ａへ登録し（ステップＳ１０６）、処理を終了する。 Then, the learning unit 14a registers the reference threshold calculated in step S103 and the reference threshold estimated in step S105 in the reference threshold information 15a (step S106), and ends the process.
　次に、例示部１４ｂおよび閾値調整部１４ｃによって実行される閾値調整処理の処理手順について図１２を用いて説明する。図１２は、閾値調整処理の処理手順を示すフローチャートである。同図に示すように、入力部１３から閾値表示要求を受け付けたならば（ステップＳ２０１）、例示部１４ｂは、基準閾値を用いて例示用画像情報１５ｂを検索する（ステップＳ２０２）。 Next, the processing procedure of the threshold adjustment process executed by the example unit 14b and the threshold adjustment unit 14c will be described with reference to FIG. FIG. 12 is a flowchart showing the processing procedure of the threshold adjustment processing. As shown in the figure, when a threshold value display request is received from the input unit 13 (step S201), the example unit 14b searches the example image information 15b using the reference threshold value (step S202).
　そして、例示部１４ｂは、抽出した例示用画像を表示部１２に対して表示する（ステップＳ２０３）。つづいて、閾値調整部１４ｃは、閾値についての確定操作を受け付けたか否かを判定し（ステップＳ２０４）、確定操作を受け付けた場合には（ステップＳ２０４，Ｙｅｓ）、最新の閾値を調整閾値情報１５ｃへ登録し（ステップＳ２０８）、処理を終了する。 Then, the example unit 14b displays the extracted example image on the display unit 12 (step S203). Subsequently, the threshold adjustment unit 14c determines whether or not a confirmation operation for the threshold has been accepted (step S204). If a confirmation operation has been accepted (Yes in step S204), the latest threshold is adjusted to the adjustment threshold information 15c. (Step S208), and the process ends.
　一方、確定操作を受け付けていない場合には（ステップＳ２０４，Ｎｏ）、閾値の変更操作を受け付けたか否かを判定する（ステップＳ２０５）。そして、変更操作を受け付けた場合には（ステップＳ２０５，Ｙｅｓ）、例示部１４ｂは、変更後の閾値を用いて例示用画像情報１５ｂを検索する（ステップＳ２０６）。 On the other hand, when the confirmation operation has not been received (step S204, No), it is determined whether or not a threshold change operation has been received (step S205). When the change operation is received (step S205, Yes), the example unit 14b searches the example image information 15b using the changed threshold value (step S206).
　そして、抽出した例示用画像を表示部１２に対して表示したうえで（ステップＳ２０７）、ステップＳ２０４以降の処理を繰り返す。なお、ステップＳ２０５の判定条件を満たさなかった場合にも（ステップＳ２０５，Ｎｏ）、ステップＳ２０４以降の処理を繰り返す。 Then, after the extracted exemplary image is displayed on the display unit 12 (step S207), the processes after step S204 are repeated. Even when the determination condition of step S205 is not satisfied (step S205, No), the processing after step S204 is repeated.
　なお、ステップＳ２０２の検索において所望する例示用画像が見つからなかった場合には、例示用画像情報１５ｂに含まれる複数の例示用画像を合成することによって所望する例示用画像を生成することとすればよい。 If a desired example image is not found in the search in step S202, a desired example image is generated by combining a plurality of example images included in the example image information 15b. Good.
　上述してきたように、本実施例では、記憶部が、紙葉類を撮像した画像データを記憶し、入力部が、判別項目ごとに用意された閾値に対する設定操作を受け付け、例示部が、受け付けられた設定操作後の閾値を用いた場合における正損判別結果の例示を、設定操作後の閾値を用いて抽出した画像データに基づいて行うこととした。したがって、正損判別用パラメータの設定を容易かつ適切に行わせることができる。 As described above, in this embodiment, the storage unit stores image data obtained by capturing paper sheets, the input unit accepts a setting operation for a threshold value prepared for each discrimination item, and the example unit accepts it. The example of the result of damage determination when the threshold value after the setting operation is used is determined based on the image data extracted using the threshold value after the setting operation. Therefore, it is possible to easily and appropriately set the parameter for determining the damage.
　なお、上述した実施例では、紙葉類識別装置が備える識別部経由で基準閾値や例示用画像を学習する場合について説明したが、他の装置で生成した基準閾値や例示画像を紙葉類識別装置の記憶部へ記憶させることとしてもよい。 In the above-described embodiment, the case where the reference threshold value and the example image are learned via the identification unit included in the paper sheet identification device has been described. However, the reference threshold value and the example image generated by another device are identified as the paper sheet. It is good also as making it memorize | store in the memory | storage part of an apparatus.
　また、上述した実施例では、閾値の変更操作と連動し、損券の実画像を切り替えて例示する場合について説明したが、損券の程度を示す画像（たとえば、イラスト）を、切り替えて例示することとしてもよい。たとえば、閾値の値に応じた複数のイラストを予め記憶しておき、閾値が変更された場合に、変更後の閾値に対応するイラストを例示することとすればよい。 Moreover, although the example mentioned above demonstrated the case where the actual image of a defective ticket was switched and illustrated in connection with the change operation of a threshold value, the image (for example, illustration) which shows the grade of a damaged ticket is switched and illustrated. It is good as well. For example, a plurality of illustrations corresponding to the threshold value may be stored in advance, and when the threshold value is changed, an illustration corresponding to the changed threshold value may be exemplified.
　以上のように、本発明に係る紙葉類判別装置および紙葉類判別方法は、正損判別用パラメータの設定を容易かつ適切に行わせたい場合に有用であり、特に、オペレータの習熟度が低い場合であっても、正損判別用パラメータの設定を容易かつ適切に行わせたい場合に適している。 As described above, the paper sheet discriminating apparatus and the paper sheet discriminating method according to the present invention are useful when it is desired to easily and appropriately set the parameters for discriminating damage, and in particular, the proficiency level of the operator is Even if it is low, it is suitable for the case where it is desired to easily and appropriately set the parameter for damage determination.
　　１０　　紙葉類判別装置
　　１１　　識別部
　　１２　　表示部
　　１３　　入力部
　　１４　　制御部
　　１４ａ　学習部
　　１４ｂ　例示部
　　１４ｃ　閾値調整部
　　１４ｄ　識別データ取得部
　　１４ｅ　正損判別部
　　１５　　記憶部
　　１５ａ　基準閾値情報
　　１５ｂ　例示用画像情報
　　１５ｃ　調整閾値情報
　１００　　紙幣処理装置
　１０１　　入金部
　１０２ａ　第１出金部
　１０２ｂ　第２出金部
　１０２ｃ　一時保留部
　１０３　　搬送部
　１０４　　識別部
　１０５　　操作表示部
　１０６　　コントローラ
　１０７　　精査カセット
　１０８ａ、１０８ｂ、１０８ｃ、１０８ｄ、１０８ｅ　スタッカ
　１０９　　回収カセット DESCRIPTION OF SYMBOLS 10 Paper sheet discrimination | determination apparatus 11 Identification part 12 Display part 13 Input part 14 Control part 14a Learning part 14b Example part 14c Threshold adjustment part 14d Identification data acquisition part 14e Integrity determination part 15 Storage part 15a Reference | standard threshold value information 15b Example image information 15c Adjustment threshold information 100 Banknote processing apparatus 101 Deposit part 102a First withdrawal part 102b Second withdrawal part 102c Temporary holding part 103 Transport part 104 Identification part 105 Operation display part 106 Controller 107 Examination cassette 108a, 108b, 108c, 108d, 108e Stacker 109 Collection cassette

Claims (8)

  1. 　紙葉類の正損を判別する紙葉類判別装置であって、
    　画像データを記憶する記憶手段と、
    　判別項目ごとに用意された閾値に対する設定操作を受け付ける受付手段と、
    　前記受付手段によって受け付けられた設定操作後の前記閾値を用いた場合における正損判別結果の例示を、当該閾値を用いて抽出した前記画像データに基づいて行う例示手段と
    　を備えたことを特徴とする紙葉類判別装置。     A paper sheet discriminating apparatus for discriminating whether a paper sheet is normal or not,
    Storage means for storing image data;
    Accepting means for accepting a setting operation for a threshold prepared for each discrimination item;
    An example means for performing an example of the result of damage determination when the threshold value after the setting operation received by the receiving unit is used based on the image data extracted using the threshold value is provided. Paper sheet discrimination device.
  2. 　前記例示手段は、
    　前記設定操作後の前記閾値に基づき、正判別の例示となる前記画像データおよび／または損判別の例示となる前記画像データを前記記憶手段から抽出することを特徴とする請求項１に記載の紙葉類判別装置。     The exemplary means includes
    2. The paper according to claim 1, wherein the image data exemplifying correct discrimination and / or the image data exemplifying loss discrimination are extracted from the storage unit based on the threshold value after the setting operation. Leaf discrimination device.
  3. 　前記記憶手段は、
    　紙葉類を撮像した撮像データを前記画像データとして記憶することを特徴とする請求項１または２に記載の紙葉類判別装置。     The storage means
    The paper sheet discriminating apparatus according to claim 1 or 2, wherein imaging data obtained by imaging a paper sheet is stored as the image data.
  4. 　前記記憶手段は、
    　前記閾値の比較対象となる比較値が前記判別項目ごとにそれぞれ関連付けられた前記画像データを記憶し、
    　前記例示手段は、
    　前記閾値以上の前記比較値と関連付けられた前記画像データおよび／または前記閾値未満の前記比較値と関連付けられた前記画像データを前記判別項目ごとにそれぞれ抽出し、抽出した前記画像データとともに当該画像データが正判別の例示であるか損判別の例示であるかの区別を表示することを特徴とする請求項３に記載の紙葉類判別装置。     The storage means
    Storing the image data in which a comparison value to be compared with the threshold is associated with each of the determination items;
    The exemplary means includes
    The image data associated with the comparison value greater than or equal to the threshold value and / or the image data associated with the comparison value less than the threshold value are extracted for each discrimination item, and the image data together with the extracted image data. The paper sheet discriminating apparatus according to claim 3, wherein a distinction between whether or not is an example of correct discrimination or an example of loss discrimination is displayed.
  5. 　前記閾値を用いて紙葉類の正損を判別する判別手段
    　をさらに備え、
    　前記判別手段は、
    　前記画像データを入力として受け付け、当該画像データについて前記判別項目ごとに算出した前記比較値を当該画像データと関連付けて前記記憶手段へ記憶させることを特徴とする請求項４に記載の紙葉類判別装置。     A discriminating means for discriminating whether the paper sheet is normal or not using the threshold;
    The discrimination means includes
    5. The paper sheet discrimination according to claim 4, wherein the image data is received as an input, and the comparison value calculated for each discrimination item for the image data is stored in the storage means in association with the image data. apparatus.
  6. 　前記例示手段は、
    　特定の前記判別項目について例示すべき前記比較値を有する前記画像データがない場合に、当該判別項目についてそれぞれ異なる前記比較値を有する前記画像データから例示すべき前記画像データを生成することを特徴とする請求項４に記載の紙葉類判別装置。     The exemplary means includes
    The image data to be exemplified is generated from the image data having the different comparison values for the discrimination item when there is no image data having the comparison value to be exemplified for the specific discrimination item. The paper sheet discrimination apparatus according to claim 4.
  7. 　前記例示手段は、
    　それぞれ異なる前記判別項目ごとに抽出された前記画像データを合成した合成データを例示することを特徴とする請求項１～６のいずれか一つに記載の紙葉類判別装置。     The exemplary means includes
    7. The paper sheet discriminating apparatus according to claim 1, wherein synthetic data obtained by synthesizing the image data extracted for each different discriminating item is exemplified.
  8. 　紙葉類の正損を判別する紙葉類判別方法であって、
    　画像データを記憶部へ記憶させる記憶工程と、
    　判別項目ごとに用意された閾値に対する設定操作を受け付ける受付工程と、
    　前記受付工程によって受け付けられた設定操作後の前記閾値を用いた場合における正損判別結果の例示を、当該閾値を用いて抽出した前記画像データに基づいて行う例示工程と
    　を含んだことを特徴とする紙葉類判別方法。     A paper sheet determination method for determining whether a paper sheet is normal or not,
    A storage step of storing image data in a storage unit;
    An accepting process for accepting a setting operation for a threshold prepared for each discrimination item;
    An example of performing an example of the result of damage determination when the threshold value after the setting operation received by the receiving step is used, based on the image data extracted using the threshold value. Paper sheet discrimination method.
PCT/JP2010/054829 2010-03-19 2010-03-19 Paper sheet identification device and paper sheet identification method WO2011114516A1 (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
PCT/JP2010/054829 WO2011114516A1 (en) 2010-03-19 2010-03-19 Paper sheet identification device and paper sheet identification method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/JP2010/054829 WO2011114516A1 (en) 2010-03-19 2010-03-19 Paper sheet identification device and paper sheet identification method

Publications (1)

Publication Number Publication Date
WO2011114516A1 true WO2011114516A1 (en) 2011-09-22

Family

ID=44648642

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/JP2010/054829 WO2011114516A1 (en) 2010-03-19 2010-03-19 Paper sheet identification device and paper sheet identification method

Country Status (1)

Country Link
WO (1) WO2011114516A1 (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10286422B2 (en) 2014-10-24 2019-05-14 Glory Ltd. Paper sheet processing device, paper sheet processing system, and paper sheet processing method

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH08305923A (en) * 1995-04-28 1996-11-22 Glory Ltd Authenticity distinguishing device
JP2009294933A (en) * 2008-06-05 2009-12-17 Toshiba Corp Paper sheet processing apparatus and paper sheet processing system

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH08305923A (en) * 1995-04-28 1996-11-22 Glory Ltd Authenticity distinguishing device
JP2009294933A (en) * 2008-06-05 2009-12-17 Toshiba Corp Paper sheet processing apparatus and paper sheet processing system

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10286422B2 (en) 2014-10-24 2019-05-14 Glory Ltd. Paper sheet processing device, paper sheet processing system, and paper sheet processing method

Similar Documents

Publication Publication Date Title
EP2827306B1 (en) Valuable media processing device
US8408398B2 (en) Sheet handling system
WO2014064776A1 (en) Sheet sorting device and sheet management system
WO2009099150A1 (en) Paper sheet processing device and paper sheet processing method
JP2013254349A (en) Paper sheet discrimination system and paper sheet discrimination method
JP2011197833A (en) Check processing apparatus and check processsing method
WO2011114516A1 (en) Paper sheet identification device and paper sheet identification method
JP2016018497A (en) Paper sheet management device, paper sheet processing system, and paper sheet management method
WO2017047642A1 (en) Paper sheet processing device and paper sheet processing method
JP2008250530A (en) Bank bill auditing device and auditing method
JP2014225214A (en) Paper sheet processing device and paper sheet processing method
JP2008225806A (en) Cash processor and its output method
JP2009294933A (en) Paper sheet processing apparatus and paper sheet processing system
JP2013182350A (en) Bill handling device
JP2003242549A (en) Foul bill extracting device and cash handling device using it
JP2014197357A (en) Valuable medium management device, valuable medium management system and valuable medium management method
JP5198557B2 (en) Banknote handling equipment
US20110139574A1 (en) Banknote handling system and banknote handling method
JP7178798B2 (en) Paper sheet handling equipment and paper sheet handling system
JP2018067254A (en) Paper sheet identification device and paper sheet handling device
JP6000582B2 (en) Valuable medium processing equipment
JP2020071639A (en) Valuable medium processing device, valuable medium processing system, and valuable medium processing method
WO2013161066A1 (en) Bill processing device
JP2009258812A (en) Paper money handling device
WO2019077792A1 (en) Banknote processing system

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 10847927

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 10847927

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: JP