CN117237966B - Banknote recognition method and device based on internal contour of denomination digital character - Google Patents
Banknote recognition method and device based on internal contour of denomination digital character Download PDFInfo
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- CN117237966B CN117237966B CN202311498517.3A CN202311498517A CN117237966B CN 117237966 B CN117237966 B CN 117237966B CN 202311498517 A CN202311498517 A CN 202311498517A CN 117237966 B CN117237966 B CN 117237966B
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- 238000000034 method Methods 0.000 title claims abstract description 27
- 238000004364 calculation method Methods 0.000 claims description 5
- 238000012935 Averaging Methods 0.000 claims description 4
- 238000012545 processing Methods 0.000 claims description 3
- 230000000717 retained effect Effects 0.000 claims description 3
- 238000010586 diagram Methods 0.000 description 3
- 235000003956 Luffa Nutrition 0.000 description 1
- 244000050983 Luffa operculata Species 0.000 description 1
- 238000005299 abrasion Methods 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000002950 deficient Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012797 qualification Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Abstract
The invention provides a paper currency recognition method and a device based on the internal outline of denomination digital characters, wherein the method comprises the following steps: s1: confirming a rectangular digital area B2 according to the denomination of the paper money to be measured; s2: projecting and cutting the rectangular digital region B2 to obtain one or more single closed contour images B'; s3: confirming one or more difference sequences P according to the pixel values of the inner contour area of the single closed contour image B'; s4: if any value in the difference sequence P is larger than a preset threshold value, the paper currency to be detected is not qualified, otherwise the paper currency to be detected is qualified. The technology can more accurately and conveniently identify the true or false, the old or new, the damaged degree and other items of paper money.
Description
Technical Field
The invention relates to the technical field of paper currency recognition, in particular to a paper currency recognition method and device based on the internal outline of denomination digital characters.
Background
At present, a method for identifying a digital area of the face of a ruffle banknote is more commonly used for identifying images of the digital area by using a neural network, calculation projection, gradient and other modes.
However, the change of denomination digital regions of the rufigwort counterfeit money with wider circulation range in the market at present is generally smaller, and the rufigwort counterfeit money is difficult to accurately identify by using the current technical method.
Disclosure of Invention
Based on the method and the device, the invention provides a banknote recognition method and a device based on the internal contour of the denomination digital character, so that the qualification degree of the banknote can be accurately identified.
In a first aspect, an embodiment of the present invention provides a banknote recognition method based on an inner contour of a denomination digital character, the method including: s1: confirming a rectangular digital area B2 according to the denomination of the paper money to be measured; s2: projecting and cutting the rectangular digital region B2 to obtain one or more single closed contour images B'; s3: confirming one or more difference sequences P according to the pixel values of the inner contour area of the single closed contour image B'; s4: if any value in the difference sequence P is larger than a preset threshold value, the paper currency to be detected is not qualified, otherwise the paper currency to be detected is qualified.
Further, S1 includes: s11: intercepting and obtaining a rough cut image A1 at a preset physical position based on the denomination of the paper money to be detected; s12: summing the gray values of all pixel points of the rough cut image A1, and dividing the sum by the total number of pixels to obtain a gray average threshold; s13: performing binarization processing on the rough cut image A1 based on the average threshold value to obtain a binarized image B1; s14: and precisely cutting the binarized image B1 to obtain a rectangular digital area B2.
Further, S14 includes: s141: performing horizontal projection on the binarized image B1 to confirm an upper boundary r1 and a lower boundary r2; s142: simultaneously, the binarized image B1 is subjected to vertical projection to confirm a left boundary r3 and a right boundary r4; s143: and precisely cutting the binarized image B1 according to the upper boundary r1, the lower boundary r2, the left boundary r3 and the right boundary r4 to obtain a rectangular digital region B2.
Further, S2 includes: s21: performing vertical projection on the rectangular digital region B2 to obtain left and right boundary sequences of each digital character; s22: cutting a rectangular digital region B2 based on the left and right boundary sequences to obtain binarized images of n single digital characters, wherein the binarized images are recorded as B21, B22 and B23. S23: the binarized image of the single digital character with the internal closed contour is retained, resulting in one or more single closed contour images B'.
Further, S3 includes: s31: counting the white pixels of each column of the single closed contour image B' to obtain the number d of the inner contour white pixels of each column; s32: respectively averaging the number d of the inner contour white pixels of all columns of each single closed contour image B' to obtain a number average value A; s33: and (3) calculating the absolute value of the difference value of the number average value A corresponding to any two adjacent single closed contour images B', and obtaining a first difference value sequence PA.
Further, S31 includes: s311: starting from the first pixel of each column, if the white pixel is suddenly changed into a black pixel, confirming the outer contour of the digital character at the position; s312: when the black pixel is suddenly changed into a white pixel, confirming the inner contour of the digital character at the position, starting counting the number of the white pixels in the inner contour until the black pixel is suddenly changed again, and ending counting, wherein the number of the white pixels in the inner contour is marked as di, wherein i is a column index; s313: the number of inner contour white pixels of all columns of a single closed contour image B' is counted in sequence, denoted d1, d2, d3..
Further, S33 is replaced with: s33-1: solving a numerical variance D for each digital character based on the numerical mean A; s33-2: and solving the absolute value of the difference value of the number variance D corresponding to any two adjacent digital characters to obtain a second difference value sequence PD.
Further, the difference sequence P includes a first difference sequence PA or a second difference sequence PD.
In a second aspect, the present embodiment provides a banknote recognition apparatus based on an inner profile of a denomination digital character, the apparatus comprising: the rough cutting module is used for confirming a rectangular digital area B2 according to the denomination of the paper money to be tested; the fine cutting module is used for projecting and cutting the rectangular digital region B2 to obtain one or more single closed contour images B'; a sequence calculation module for determining one or more difference sequences P from the pixel values of the inner contour region of the single closed contour image B'; and the comparison module is used for judging whether the paper currency to be detected is qualified if any value in the difference value sequence P is larger than a preset threshold value, or not judging whether the paper currency to be detected is qualified.
The embodiment of the invention has the following beneficial effects:
the application provides a banknote recognition method and device based on an inner contour of a denomination digital character, wherein the method comprises the following steps: s1: confirming a rectangular digital area B2 according to the denomination of the paper money to be measured; s2: projecting and cutting the rectangular digital region B2 to obtain one or more single closed contour images B'; s3: confirming one or more difference sequences P according to the pixel values of the inner contour area of the single closed contour image B'; s4: if any value in the difference sequence P is larger than a preset threshold value, the paper currency to be detected is not qualified, otherwise the paper currency to be detected is qualified. The technology can more accurately and conveniently identify the true or false, the old or new, the damaged degree and other items of paper money.
Additional features and advantages of the invention will be set forth in the description which follows, or in part will be obvious from the description, or may be learned by practice of the invention.
In order to make the above objects, features and advantages of the present invention more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a banknote recognition method based on the internal profile of denomination digital characters according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a qualified digital area B2 of the luffa denomination according to an embodiment of the present invention;
fig. 3 is a schematic diagram of a defective lub denomination digital region B2 according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Examples
The invention provides a paper currency identification method based on the internal contour of denomination digital characters, as shown in fig. 1, specifically comprising the following steps:
s1: and confirming the rectangular digital area B2 according to the denomination of the paper money to be tested.
Fig. 2 and 3 are schematic diagrams of rectangular digital areas B2 of a pass and fail lub.
S1 comprises the following steps:
s11: and cutting out the rough cutting image A1 at a preset physical position based on the denomination of the paper money to be detected.
S12: and summing the gray values of all the pixel points of the rough cut image A1, and dividing the sum by the total number of pixels to obtain a gray average threshold value.
S13: and carrying out binarization processing on the rough cut image A1 based on the average threshold value to obtain a binarized image B1.
S14: and precisely cutting the binarized image B1 to obtain a rectangular digital area B2.
S141: the binarized image B1 is horizontally projected to confirm the upper boundary r1 and the lower boundary r2.
S142: at the same time, the binarized image B1 is vertically projected to confirm the left and right boundaries r3 and r4.
S143: and precisely cutting the binarized image B1 according to the upper boundary r1, the lower boundary r2, the left boundary r3 and the right boundary r4 to obtain a rectangular digital region B2.
S2: the rectangular digital region B2 is projected and cut to obtain one or more individual closed contour images B'.
S2 comprises the following steps:
s21: and (3) vertically projecting the rectangular digital region B2 to obtain a left boundary sequence and a right boundary sequence of each digital character.
S22: and cutting the rectangular digital region B2 based on the left boundary sequence and the right boundary sequence to obtain binarized images of n single digital characters, wherein the binarized images are recorded as B21, B22 and B23.
Where n is the number of digital characters, such as 2000 denominations, then n=4.
S23: the binarized image of the single digital character with the internal closed contour is retained, resulting in one or more single closed contour images B', noted B22, B23.
Specifically, the denominations of a typical currency all have a closed contour (e.g., 0) except for the first one that does not have an internal closed contour, B22, B23..b2n is the screened image (i.e., the one or more single closed contour images B'), i.e., the first digit "2" in the denomination is removed.
Specifically, if the denomination is 2000, then "2" will be culled, leaving only 3 "0" s, i.e. only a single closed contour image B'.
This embodiment is not applicable to notes where all denominations have no closed profile.
S3: one or more sequences of differences P are identified from the pixel values of the inner contour region of the single closed contour image B'.
S3 the calculation of the difference sequence P is performed for each image B22, B23.
Specifically, for a banknote of 2000 denominations, 2 sequences of differences P can be obtained here.
S3 comprises the following steps:
s31: and counting the white pixels of each column of the single closed contour image B' to obtain the number d of the inner contour white pixels of each column.
S311: starting from the first pixel in each column, if a sudden change of white pixels to black pixels is encountered, the position is confirmed as the outline of the digital character.
S312: when the black pixel is suddenly changed into a white pixel, the inner outline of the digital character at the position is confirmed, the number of the white pixels in the inner outline starts to be counted until the black pixel is suddenly changed again, the counting is finished, and the number of the white pixels in the inner outline is marked as di, wherein i is a column index.
S313: the number of inner contour white pixels of all columns of a single closed contour image B' is counted in sequence, denoted d1, d2, d3..
Here, the digital outline of B' is assumed to be a black pixel background and a white pixel.
S32: and (3) respectively averaging the number d of the inner contour white pixels of all columns of each single closed contour image B' to obtain a number average value A.
；
To this end, several of the denomination figures are closed inner contours, and several number average values A are obtained.
S33: and (3) calculating the absolute value of the difference value of the number average value A corresponding to any two adjacent single closed contour images B', and obtaining a first difference value sequence PA.
S33 may be replaced by:
s33-1: for each digital character, solving a numerical variance D based on a numerical mean a:
；
s33-2: and solving the absolute value of the difference value of the number variance D corresponding to any two adjacent digital characters to obtain a second difference value sequence PD.
S4: if any value in the difference sequence is larger than a preset threshold value, the paper currency to be detected is not qualified, otherwise the paper currency to be detected is qualified.
Wherein P comprises a first difference sequence PA and a second difference sequence PD.
Specifically, the thresholds corresponding to the first difference value sequence PA and the second difference value sequence PD are different, the thresholds are stable thresholds obtained by calculating qualified sample banknotes with good quality, and the values of the thresholds are different due to the fact that the denominations of the thresholds are different.
According to the method, the new and old identification, the true and false identification (the sealed area of the counterfeit money is smaller generally), the abrasion degree and the like of the paper money can be realized by calculating the pixel value in the denomination digital sealed outline, and the method can be used as an auxiliary means to enable the identification result to be more accurate while reducing the calculated amount.
Examples
The present embodiment provides a banknote recognition apparatus based on an inner contour of a denomination number character, the apparatus including:
the rough cutting module is used for confirming a rectangular digital area B2 according to the denomination of the paper money to be tested;
the fine cutting module is used for projecting and cutting the rectangular digital region B2 to obtain one or more single closed contour images B';
a sequence calculation module for determining one or more difference sequences P from the pixel values of the inner contour region of the single closed contour image B';
and the comparison module is used for judging whether the paper currency to be detected is qualified if any value in the difference value sequence P is larger than a preset threshold value, or not judging whether the paper currency to be detected is qualified.
The banknote recognition device based on the internal contour of the denomination digital character provided by the embodiment of the invention has the same implementation principle and the same technical effects as those of the banknote recognition method based on the internal contour of the denomination digital character, and for the sake of brevity, the corresponding contents in the foregoing method embodiments can be referred to for the description of the device embodiment part.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention.
Claims (8)
1. A banknote recognition method based on an internal profile of a denomination number character, the method comprising:
s1: confirming a rectangular digital area B2 according to the denomination of the paper money to be measured;
s2: projecting and cutting the rectangular digital region B2 to obtain a plurality of single closed contour images B';
s3: confirming one or more difference sequences P according to the pixel values of the inner contour area of the single closed contour image B';
s4: if any value in the difference sequence P is larger than a preset threshold value, the paper currency to be detected is not qualified, otherwise the paper currency to be detected is qualified;
s3 comprises the following steps:
s31: counting the white pixels of each column of the single closed contour image B' to obtain the number d of the inner contour white pixels of each column;
s32: respectively averaging the number d of the inner contour white pixels of all columns of each single closed contour image B' to obtain a number average value A;
s33: and obtaining the absolute value of a difference value of the number average value A corresponding to any two adjacent single closed contour images B', and obtaining a first difference value sequence PA, wherein the difference value sequence P comprises the first difference value sequence PA.
2. The method of claim 1, wherein S1 comprises:
s11: intercepting and obtaining a rough cut image A1 at a preset physical position based on the denomination of the paper money to be detected;
s12: summing the gray values of all pixel points of the rough cut image A1, and dividing the sum by the total number of pixels to obtain a gray average threshold;
s13: performing binarization processing on the rough cut image A1 based on the average threshold value to obtain a binarized image B1;
s14: and precisely cutting the binarized image B1 to obtain a rectangular digital area B2.
3. The method of claim 2, wherein S14 comprises:
s141: performing horizontal projection on the binarized image B1 to confirm an upper boundary r1 and a lower boundary r2;
s142: simultaneously, the binarized image B1 is subjected to vertical projection to confirm a left boundary r3 and a right boundary r4;
s143: and precisely cutting the binarized image B1 according to the upper boundary r1, the lower boundary r2, the left boundary r3 and the right boundary r4 to obtain a rectangular digital region B2.
4. The method of claim 2, wherein S2 comprises:
s21: performing vertical projection on the rectangular digital region B2 to obtain left and right boundary sequences of each digital character;
s22: cutting a rectangular digital region B2 based on the left and right boundary sequences to obtain binarized images of n single digital characters, wherein the binarized images are recorded as B21, B22 and B23.
S23: the binarized image of the single digital character with the internal closed contour is retained, resulting in one or more single closed contour images B'.
5. The method of claim 4, wherein S31 comprises:
s311: starting from the first pixel of each column, if the white pixel is suddenly changed into a black pixel, confirming the outer contour of the digital character at the position;
s312: when the black pixel is suddenly changed into a white pixel, confirming the inner contour of the digital character at the position, starting counting the number of the white pixels in the inner contour until the black pixel is suddenly changed again, and ending counting, wherein the number of the white pixels in the inner contour is marked as di, wherein i is a column index;
s313: the number of inner contour white pixels of all columns of a single closed contour image B' is counted in sequence, denoted d1, d2, d3..
6. The method of claim 1, wherein S33 is replaced with:
s33-1: solving a numerical variance D for each digital character based on the numerical mean A;
s33-2: and solving the absolute value of the difference value of the number variance D corresponding to any two adjacent digital characters to obtain a second difference value sequence PD.
7. The method of claim 6, wherein the difference sequence P comprises a first difference sequence PA or a second difference sequence PD.
8. A banknote recognition device based on an internal profile of a denomination number character, the device comprising:
the rough cutting module is used for confirming a rectangular digital area B2 according to the denomination of the paper money to be tested;
a fine cutting module for projecting and cutting the rectangular digital region B2, a plurality of single closed contour images B';
a sequence calculation module for determining one or more difference sequences P from the pixel values of the inner contour region of the single closed contour image B';
the comparison module is used for judging whether the paper currency to be detected is qualified if any value in the difference value sequence P is larger than a preset threshold value, if not, the paper currency to be detected is qualified;
the sequence calculating module further comprises: counting the white pixels of each column of the single closed contour image B' to obtain the number d of the inner contour white pixels of each column; respectively averaging the number d of the inner contour white pixels of all columns of each single closed contour image B' to obtain a number average value A; and obtaining the absolute value of a difference value of the number average value A corresponding to any two adjacent single closed contour images B', and obtaining a first difference value sequence PA, wherein the difference value sequence P comprises the first difference value sequence PA.
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| CN105957238B (en) * | 2016-05-20 | 2019-02-19 | 聚龙股份有限公司 | A kind of paper currency management method and its system |
| JP6976455B2 (en) * | 2018-10-24 | 2021-12-08 | 富士通フロンテック株式会社 | Banknote inspection device, banknote inspection method and banknote inspection program |
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| CN108460775A (en) * | 2017-02-17 | 2018-08-28 | 深圳怡化电脑股份有限公司 | A kind of forge or true or paper money recognition methods and device |
| CN107067533A (en) * | 2017-04-14 | 2017-08-18 | 深圳怡化电脑股份有限公司 | The method and device that a kind of bank note differentiates |
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