JP2020134964A - Image processing apparatus and image processing method - Google Patents

Abstract

To read an image of a document and classify the image of the document based on the characteristics of the entire image of the document.SOLUTION: In an image processing apparatus 10, when a training mode is set by an operation from an operation unit (operation unit 22 or touch panel 23), a control unit 31 extracts the characteristics of an image of a document read by an image reading unit 11, and learns learning data from which the type of the document input by the operation from the operation unit is derived based on the characteristics of the document. When a classification mode is set by an operation from the operation unit, the control unit 31 extracts the characteristics of the image of the document read by the image reading unit 11, determines the type of the document based on the characteristics of the image of the document and the learning data, and adds information indicating the determined type to image data of the document.SELECTED DRAWING: Figure 2

Description

The present invention relates to an image processing apparatus and an image processing method for acquiring image data of a manuscript, and more particularly to a technique for classifying image data of a manuscript.

Image processing devices include multifunction devices (MFPs), copiers, facsimile machines, and the like. In such an image processing apparatus, it is desirable that the image data of the scanned document can be automatically classified.

For example, in the document management system described in Patent Document 1, the read document image data is divided into a text area, a table area, a ruled line, a figure area, and the like, layout information of each area is extracted, and the document is specified in the image data of the document. Character recognition or bar code recognition is executed for the area at the position of, and the information read by character recognition or bar code recognition is added to the document image data as an attribute and saved, and the document image is used by using that attribute. The data can be searched.

JP-A-2002-342343

However, in the document management system described in Patent Document 1, since the information read by character recognition or bar code recognition for a region at a specific position in the document image is used as an attribute of the document image, this attribute is a specific position. It is hard to say that the document images are properly classified because they are based on the characteristics of the area of the document image and not based on the characteristics of the entire document image.

The present invention has been made in view of the above circumstances, and an object of the present invention is to classify the images of the manuscript based on the characteristics of the entire image of the manuscript.

The image processing device according to one aspect of the present invention includes an image acquisition unit for acquiring image data of a document, an operation unit for inputting settings and instructions by user operation, and a training mode according to instructions input to the operation unit. Alternatively, the control unit includes a control unit for setting the classification mode, and the control unit is an image of a manuscript whose image data has been acquired by the image acquisition unit when the training mode setting is input by operating the operation unit. When the characteristics of the original are extracted, data indicating the characteristics of the image of the original is collected as learning data for determining the type of the original, and the classification mode is set by operating the operation unit. , The feature of the image of the manuscript whose image data has been acquired by the image acquisition unit is extracted, the type of the manuscript is discriminated based on the feature of the image of the manuscript and the learning data, and the information indicating the discriminated type is obtained. It is added to the image data of the manuscript.

Further, the image processing method according to one aspect of the present invention includes an image acquisition step for acquiring image data of a document, an operation step for accepting an operation of an operation unit by a user, and a training mode set in the operation step. , The feature of the image of the manuscript whose image data was acquired in the image acquisition step is extracted, and the data showing the feature of the image of the manuscript is collected as learning data for discriminating the type of the manuscript, and the operation is performed. When the classification mode is set in the step, the characteristics of the image of the manuscript whose image data was acquired in the image acquisition step are extracted, and the type of the manuscript is determined based on the characteristics of the image of the manuscript and the learning data. It includes a control step of discriminating and adding information indicating the discriminated type to the image data of the manuscript.

According to the present invention, an image of a manuscript can be read and the image of the manuscript can be classified based on the characteristics of the entire image of the manuscript.

It is sectional drawing which shows the image processing apparatus which concerns on one Embodiment of this invention. It is a block diagram which shows the main internal structure of the image processing apparatus of this embodiment. It is a flowchart which shows the processing procedure of the training mode for generating the learning data of deep learning. It is a figure which shows the classification function input screen displayed on the display part. It is a figure which shows the training mode setting screen displayed on the display part. It is a figure which shows the concept of deep learning. It is a chart exemplifying the correspondence between the hue, contrast, the number of colors, and the density of the image, which are the features of the image of the manuscript, and the invitation and the receipt, which are the tag names. It is a flowchart which shows the processing procedure in a classification mode. It is a figure which shows the classification mode setting screen displayed on the display part. It is a figure which shows the pop-up screen displayed on the display part. It is a figure which shows the other pop-up screen displayed on the display part. It is a figure which shows the property of the image of the original which was displayed on the display part. It is a figure which shows the other property of the image of a manuscript displayed on the display part. It is a figure which shows the list of the file displayed on the display part. It is a figure which shows an example of the transmission setting screen displayed on the display part.

Hereinafter, the image processing apparatus according to the embodiment of the present invention will be described with reference to the drawings.

FIG. 1 is a cross-sectional view showing an image processing apparatus according to an embodiment of the present invention. As shown in FIG. 1, the image processing apparatus 10 of the present embodiment is an MFP (image forming apparatus) having a plurality of functions such as a copy function, a printer function, and a facsimile function. The image processing device 10 includes an image reading unit 11 and an image forming unit 12.

The image reading unit 11 has an image sensor that optically reads the image of the document, and the analog output of the image sensor is converted into a digital signal to generate image data indicating the image of the document. The image reading unit 11 is an example of an image acquisition unit within the scope of claims.

The image forming unit 12 forms the image indicated by the image data on the recording paper, and is used for the image forming unit 3M for magenta, the image forming unit 3C for cyan, the image forming unit 3Y for yellow, and the black. The image forming unit 3Bk of the above is provided. In each of the image forming units 3M, 3C, 3Y, and 3Bk, the surface of the photoconductor drum 4 is uniformly charged, the surface of the photoconductor drum 4 is exposed, and an electrostatic latent image is formed on the surface of the photoconductor drum 4. Is formed, the electrostatic latent image on the surface of the photoconductor drum 4 is developed into a toner image, and the toner image on the surface of the photoconductor drum 4 is primarily transferred to the intermediate transfer belt 5. As a result, a color toner image is formed on the intermediate transfer belt 5. The toner image of this color is secondarily transferred to the recording paper P conveyed from the paper feeding unit 14 through the transfer path 8 in the nip area N between the intermediate transfer belt 5 and the secondary transfer roller 6.

After that, the recording paper P is heated and pressurized by the fixing device 15, the toner image on the recording paper P is fixed by thermocompression bonding, and the recording paper P is discharged to the discharge tray 17 through the discharge roller 16.

Next, FIG. 2 is a block diagram showing a main internal configuration of the image processing device 10 of the present embodiment. As shown in FIG. 2, the image processing device 10 of the present embodiment includes an image reading unit 11, an image forming unit 12, a display unit 21, an operation unit 22, a touch panel 23, a network interface unit 91, and an image memory. 26, an image processing unit 27, a storage unit 28, and a control unit 29 are provided. These components are capable of transmitting and receiving data or signals through the bus to each other.

The display unit 21 is composed of a liquid crystal display (LCD), an organic EL (OLED: Organic Light-Emitting Diode) display, and the like. The operation unit 22 includes physical keys such as a numeric keypad, an enter key, and a start key.

A touch panel 23 is arranged on the screen of the display unit 21. The touch panel 23 is a touch panel of a so-called resistance film type or capacitance type, and detects contact (touch) of a user's finger or the like with the touch panel 23 together with the contact position, and a detection signal indicating the coordinates of the contact position. Is output to the control unit 31 or the like described later of the control unit 29.

The network interface unit 91 transmits and receives various data to and from an external device such as a terminal device or a server in the local area or on the Internet. The network interface unit 91 is an example of an image acquisition unit within the scope of claims. The image processing apparatus according to the embodiment of the present invention includes at least one of an image reading unit 11 and a network interface unit 91.

The image memory 26 stores image data indicating an image of the original document read by the image reading unit 11. Further, the control unit 31 functions as an image processing unit 27, and as the image processing unit 27, performs various image processing such as shading correction on the image data in the image memory 26.

The image processing unit 27 may be provided separately from the control unit 31 and the control unit 29. In this case, the control unit 31 and the image processing unit 27 are examples of the control unit within the scope of the claims.

The storage unit 28 is a large-capacity storage device such as an SSD (Solid State Drive) or an HDD (Hard Disk Drive), and stores various application programs and various data.

The control unit 29 is composed of a processor, a RAM (Random Access Memory), a ROM (Read Only Memory), and the like. The processor is, for example, a CPU (Central Processing Unit), an ASIC (Application Specific Integrated Circuit), an MPU (Micro Processing Unit), or the like. The control unit 29 functions as the control unit 31 when the control program stored in the ROM or the storage unit 28 is executed by the processor.

The control unit 31 comprehensively controls the image processing device 10. The control unit 29 is connected to an image reading unit 11, an image forming unit 12, a display unit 21, an operation unit 22, a touch panel 23, a network interface unit 91, an image memory 26, an image processing unit 27, a storage unit 28, and the like. .. The control unit 31 controls the operation of these components and transmits / receives signals or data to / from each component.

The control unit 31 serves as a processing unit that executes various processes necessary for image formation by the image processing device 10. Further, the control unit 31 receives an operation instruction input by the user based on the detection signal output from the touch panel 23 or the operation of the physical key of the operation unit 22. Further, the control unit 31 has a function of controlling the display operation of the display unit 21 and a function of controlling the communication operation of the network interface unit 91.

In the image processing device 10 having such a configuration, for example, when the user sets the document in the image reading unit 11 and operates the operation unit 22 or the touch panel 23 to instruct the execution of copying, the control unit 31 displays the image. The image of the original is read by the reading unit 11, and the image data indicating the image of the original is stored in the image memory 26. The image processing unit 27 performs various processes on the image data. The image forming unit 12 acquires image data from the image memory 26 and forms an image of the original document indicated by the image data on the recording paper.

Further, the user operates the operation unit 22 or the touch panel 23 to input a tag name indicating the type of the original, a training mode setting, and an instruction to execute the image reading of the original by the image reading unit 11, and the control unit 31 presses. When the training mode setting, the tag name, and the reading execution instruction are received, the control unit 31 sets the image processing device 10 to the training mode, and the image reading unit 11 reads the image of the original to the image memory 26. The image data is stored and the image data is processed by the image processing unit 27. Further, the control unit 31 executes a process of extracting the features of the image of the scanned document based on the image data stored in the image memory 26. Then, the control unit 31 collects the data showing the characteristics of the image of the manuscript extracted in this way as learning data for discriminating the type of the manuscript indicated by the tag name.

After that, the user sets a further document in the image reading unit 11, and the user operates the operation unit 22 or the touch panel 23 to set the classification mode and execute the image reading unit 11 to read the image of the document. When the instruction is received, the control unit 31 sets the classification mode, the image reading unit 11 reads the image of the original, the image data 26 stores the image data, and the image processing unit 27 processes the image data. To do. After that, the control unit 31 further executes a process of extracting the features of the image of the manuscript based on the image data in the image memory 26, and collects the features of the image of the manuscript and as described above. Based on the learning data, the tag name indicating the type of the manuscript is determined, and the information indicating the determined tag name is added to the image data of the manuscript. That is, the control unit 31 classifies the other manuscript by adding the information indicating the tag name in this way.

Therefore, in the state where the control unit 31 is set to the training mode, the data indicating the characteristics of the image of the document read by the image reading unit 11 is the document indicated by the tag name input to the operation unit 22 or the touch panel 23. It is collected as training data for determining the type of. Further, in the state where the control unit 31 is set to the classification mode, the characteristics of the image of the original document extracted from the image of the original document read by the image reading unit 11 and the learning data collected at this time are obtained. By comparing, the tag name associated with the learning data indicating the feature that matches or approximates the feature of the image of the manuscript is determined. Then, the control unit 31 classifies the image of the scanned document by adding the information indicating the tag name to the image of the scanned document. Even when the classification mode is set, the control unit 31 collects data indicating the characteristics of the image of the original document read by the image reading unit 11 as learning data for determining the tag name.

Next, the processing procedure in the training mode will be described in detail with reference to the flowchart shown in FIG.

First, the administrator (specific user) operates the operation unit 22 or the touch panel 23 to input an instruction to execute the document classification function. When the operation unit 22 or the touch panel 23 receives the input of the instruction, the control unit 31 causes the display unit 21 to display the classification function input screen G1 as shown in FIG. 4 in response to the instruction (S101). In the classification function input screen G1 shown in FIG. 4, a key K11 for inputting a training mode execution instruction, a key K12 for inputting a classification mode execution instruction, and a text box for inputting an identification information ID TB1 is displayed.

By the operation of the operation unit 22 or the touch panel 23 by the administrator, the identification information ID of the administrator is input to the text box TB1, and when the training mode key K11 is pressed, the control unit 31 is input to the text box TB1. Authenticate whether or not the identification information ID matches the identification information ID of the administrator (S102). When the control unit 31 determines that the identification information ID entered in the text box TB1 matches the identification information ID of the administrator (YES in S102), the image processing device 10 is set to the training mode (S103). If the control unit 31 determines that the identification information ID entered in the text box TB1 does not match the identification information ID of the administrator (NO in S102), the control unit 31 does not set the training mode. The process ends.

When the administrator sets the document in the image reading unit 11 and operates the operation unit 22 or the touch panel 23 in the state where the training mode is set in this way, the control unit 31 inputs a scan execution instruction. In accordance with the input scan execution instruction, the image reading unit 11 reads the image of the original, and the image data 26 indicating the image of the original output from the image reading unit 11 is stored in the image memory 26. The image processing unit 27 performs various processes on the image data (S104). The various processes performed by the image processing unit 27 in this training mode are preliminary processes performed in order to accurately extract the features of the image of the original in S109 described later, and are image density, contrast, edges, and corners. It is a process to emphasize these.

After that, the control unit 31 causes the display unit 21 to display the training mode setting screen G2 as shown in FIG. 5 (S105). On the training mode setting screen G2 shown in FIG. 5, the image D1 of the document read by the image reading unit 11, and the keys K21, K22, K23, K24 associated with each tag name already set, are displayed. K25 ..., a text box TB2 for entering a tag name indicating a new type of manuscript, and an OK key 20 are displayed.

When the administrator confirms the image D1 of the manuscript displayed on the display unit 21 and determines that any of the tag names indicated by the keys K21, K22, ... Indicates the type of the manuscript, each key Of K21, K22, ..., Touch operation is performed on the key indicating the tag name indicating the type of the manuscript, and none of the tag names indicated by the keys K21, K22, ... Is the tag name indicating the type of the manuscript. If it is determined, the operation unit 22 or the touch panel 23 is operated, a tag name indicating a new document type is input into the text box TB2, and the OK key 20 is touch-operated.

When the control unit 31 detects a touch operation of any of the keys K21, K22, ... Through the touch panel 23 (S106 "key"), the control unit 31 acquires the tag name associated with the instructed key (S107). .. Further, when the control unit 31 detects the touch operation of the OK key 20 through the touch panel 23 (S106 “tag name”), the control unit 31 acquires the tag name input in the text box TB2 (S108).

Then, the control unit 31 analyzes the image data indicating the image D1 of the original in the image memory 26 and extracts the features of the image D1 of the original (S109).

Further, the control unit 31 uses the data indicating the characteristics of the image D1 of the manuscript extracted in S109, which is indicated by the tag name acquired in S107 or S108, for example, learning data for deep learning for determining the type of the manuscript. Collect as (S110). The control unit 31 stores the deep learning learning data in the storage unit 28 (S110).

In the above, the target of image data analysis and image feature extraction by the control unit 31 is the original image read by the image reading unit 11, but instead, the network interface unit 91 An image acquired from the terminal device, server, or the like may be a target for image data analysis and image feature extraction.

For example, the tag name indicating the type of manuscript may be a contract, receipt, certificate, invitation, poster, or the like. Further, the control unit 31 extracts elements such as hue, contrast, number of colors, and density of the image as features of the image of the original by the image processing unit 27. The learning data is used for learning by the deep learning performed by the control unit 31.

For example, the administrator sets the invitation card in the image reading unit 11, operates the operation unit 22 or the touch panel 23, inputs the training mode setting, inputs the "invitation card" as the tag name, and scans. It is assumed that the execution is instructed. The control unit 31 acquires an "invitation card" as a tag name, causes the image reading unit 11 to read the image of the invitation card, stores the image data indicating the image of the invitation card in the image memory 26, and stores the image data indicating the image of the invitation card in the image processing unit. 27 causes various processing on the image data. Then, the control unit 31 extracts the features of the image of the invitation and collects the data showing the features of the image of the invitation as learning data for determining the type of the manuscript.

FIG. 7 is Chart A illustrating the correspondence between the hue, contrast, number of colors, and density of the image, which are the characteristics of the image of the manuscript, and the invitation and the receipt, which are the tag names. In the training mode, as shown in Chart A of FIG. 7, an example is shown in which a combination of hue, contrast, number of colors, and density of an image is used as an image feature corresponding to each tag name input by the administrator. There is. In this example, an invitation and a receipt are used as the tag name.

With the training mode set, the administrator sets the document in the image reading unit 11, operates the operation unit 22 or the touch panel 23, inputs a tag name indicating the type of the document, and executes scanning. Instruct. The control unit 31 extracts the characteristics of the image of the original document read by the image reading unit 11, and collects data indicating the characteristics of the image of the original document as learning data for determining the type of the original document.

In the present embodiment, deep learning is used for the process for determining the type of the original. FIG. 6 is a diagram showing a concept of deep learning performed by the control unit 31. In the deep learning shown in FIG. 6, the features of the image are input from the input layer IL, and the tag name is output from the output layer OL via the intermediate layer (plurality of nodes ML). By adjusting how each node ML is connected, or "what kind of input and what kind of output is performed" by each node ML, that is, learning data is learned and generated. By doing or updating, the tag name to be acquired is determined based on the characteristics of the input image and output.

In this deep learning, in the training mode, each element indicating the characteristics of the image input from the input layer IL as the characteristics of the image corresponding to the tag name input by the administrator is stored as the node ML. The set of the node ML is an example of learning data.

Further, in the classification mode, the output layer OL tentatively determines the tag name corresponding to the feature of the image input from the input layer IL and outputs the tag name. When this provisional tag name is approved by the administrator as indicating a formal tag name, each node ML is stored in association with the approved tag name. If this provisional tag name is changed by the administrator, each node ML is updated until the provisionally determined tag name matches the tag name changed by the administrator. Updates are made (ie, the training data is updated). The deeper the hierarchy of each node ML and the larger the number of each node ML, the more complicated classification and judgment can be performed, and the tag name corresponding to the feature of the input image can be accurately determined. Therefore, the deep learning learning data is more effective because the number of images of the manuscript processed in the training mode and the classification mode increases, which deepens the hierarchy of each node ML and increases each node ML. Become. Preferably, the training mode is such that the correct tag name acquisition rate (the rate at which the tentatively determined tag name is approved by the administrator as indicating the official tag name) is, for example, 95% or more. Try to increase the number of images of the original processed in.

In addition, the control unit 31 uses the characteristics of the image of the original document collected in association with the tag name without using deep learning in the process for determining the type of the original, and the tag name and the image of the collected original document. The type of the original may be determined based on the correspondence with the characteristics of.

Next, the processing procedure in the above classification mode will be described in detail with reference to the flowchart shown in FIG.

The user operates the operation unit 22 or the touch panel 23 to instruct the execution of the document classification function. The control unit 31 causes the display unit 21 to display the classification function input screen G1 shown in FIG. 4 in response to an instruction to execute the document classification function (S201).

The user operates the operation unit 22 or the touch panel 23 to enter his / her identification information ID in the text box TB1 and operates the key K12 in the classification mode. The control unit 31 sets the classification mode when the classification mode setting instruction is input to the operation unit 22 or the touch panel 23 based on this operation (YES in S202) (S203). If there is no input of this setting instruction (NO in S202), the process ends.

With the classification mode set in this way, when the user sets the document in the image reading unit 11 and operates the operation unit 22 or the touch panel 23 to instruct the execution of scanning, the control unit 31 reads the image. The image of the original is read by the unit 11, the image data indicating the image of the original is stored in the image memory 26, and the image processing unit 27 performs various processing on the image data (S204). Similar to the training mode, the various processes performed by the image processing unit 27 in this classification mode are also the processes for emphasizing the density, contrast, edges, and corners of the image, which are performed to accurately extract the features of the image of the original. is there.

Further, the control unit 31 causes the display unit 21 to display the classification mode setting screen G3 as shown in FIG. 9 (S205). On the classification mode setting screen G3 shown in FIG. 9, the image data D2 of the document read by the image reading unit 11 and the start key K31 are displayed.

For example, when the user touches the start key K31 displayed on the display unit 21, the control unit 31 receives an instruction to execute the classification process based on the touch operation of the start key K31 through the touch panel 23, and follows this instruction. The image processing unit 27 analyzes the image data indicating the image data D2 of the original in the image memory 26, extracts the characteristics of the image data D2 of the original, and stores the characteristics of the image data D2 of the original and the storage unit 28. The tag name of the image data D2 of the manuscript is determined based on the learning data obtained by the above-mentioned deep learning (S206). Then, the control unit 31 displays the pop-up screen P1 as shown in FIG. 10 in the classification mode setting screen G3. On the pop-up screen P1 shown in FIG. 10, the OK key K32 and the NG key K33 are displayed together with the tag name of the image data D2 of the original document determined in S206.

When the user determines that the tag name of the image data D2 of the document on the pop-up screen P1 is correct, he / she touches the OK key K32. When the control unit 31 detects the touch operation of the OK key K32 through the touch panel 23 (S207 “OK”), the control unit 31 adds information indicating the tag name determined in S206 to the image data D2 of the document (S208). That is, the control unit 31 classifies the image data D2 of the original. Then, the control unit 31 updates the deep learning learning data for determining the tag name indicating the type of the manuscript by using the feature of the image data D2 of the manuscript extracted in S206, and stores this learning data. It is stored in 28 (S209).

Further, when the user determines that the tag name of the image data D2 of the document on the pop-up screen P1 is incorrect, the user touches the NG key K33. When the control unit 31 detects the touch operation of the NG key K33 through the touch panel 23 (S207 “NG”), the control unit 31 displays the pop-up screen P2 as shown in FIG. 11 in the classification mode setting screen G3. In the pop-up screen P2 shown in FIG. 11, a text box TB3 for inputting a tag name indicating a new document type, and keys K21, K22, and K23 associated with each tag name already set are displayed. , K24, K25 ..., And OK key K41 are displayed.

The user confirms the image data D2 of the manuscript displayed on the display unit 21, and if any of the keys K21, K22, ... Is associated with the tag name indicating the type of the manuscript, the corresponding key. When the control unit 31 detects a touch operation of any of the keys K21, K22, ... Through the touch panel 23, the control unit 31 assigns a tag name indicating the type of the document associated with the operated key. Obtained as a formal tag name to be added to the image data of the manuscript (S210). On the other hand, if none of the keys K21, K22, ... Is associated with the tag name indicating the type of the original, the user operates the operation unit 22 or the touch panel 23 to indicate the new type of the original. Is entered in the text box TB3, and the OK key K41 is touch-operated. When the control unit 31 detects the touch operation of the OK key K41, the control unit 31 acquires the tag name indicating the new document type entered in the text box TB3 as the official tag name to be added to the image of the document. (S210). The control unit 31 adds information indicating the tag name acquired in S210 to the image data D2 of the original.

Then, the control unit 31 collects the characteristics of the image data D2 of the manuscript used here as learning data for discriminating the tag name acquired in S210, and uses the characteristics of the image data D2 of the manuscript to describe the above. In this way, the learning data is updated by deep learning, and the updated learning data is stored in the storage unit 28 (S209).

In the above, the target of image data analysis and image feature extraction by the control unit 31 is the original image read by the image reading unit 11, but instead, the network interface unit 91 An image acquired from the terminal device, server, or the like may be a target for image data analysis and image feature extraction.

Therefore, the user can automatically classify the image of the original by simply setting the classification mode and having the image reading unit 11 read the image of the original, and the image of the original is correctly classified. If not, it is possible to input the correct tag name indicating the new manuscript type and update the deep learning learning data so that the input correct tag name is derived based on the characteristics of the image of the new manuscript. ..

12 to 15 show an example of adding information indicating a tag name to the image data of the manuscript.

FIG. 12 shows the property PR1 of the image of the original displayed on the display unit 21. The control unit 31 adds information indicating a tag name for the image of the manuscript to the image data of the manuscript, stores it together with the image data of the manuscript, and displays the tag name for the image of the manuscript. In the property PR1 as, it is displayed in the item Q1 (document type) as one of the display items.

FIG. 13 shows the property PR2 of the image of the original displayed on the display unit 21. The control unit 31 adds information indicating a tag name for the image of the manuscript to the image data of the manuscript, stores it together with the image data of the manuscript, and displays the tag name for the image of the manuscript. In the property PR2 as, it is displayed in the item Q2 (tag) as one of the display items.

FIG. 14 shows a list FR of files displayed on the display unit 21 by the control unit 31. The control unit 31 adds information indicating a tag name for the image of the manuscript to the image data of the manuscript and stores it together with the image data of the manuscript, and stores the tag name as an image indicating a list of files. It is displayed in the item Q3 of the tag name in the image FR.

FIG. 15 shows an example of a transmission setting screen displayed on the display unit 21 by the control unit 31. The control unit 31 adds information indicating a tag name for the image of the manuscript to the image data of the manuscript and stores it together with the image data of the manuscript, and stores the image data of the manuscript together with the image data of the manuscript, for example, a computer connected to the image processing device 10. When the setting screen G5 for setting the transmission when transmitting to the folder in the storage unit is displayed on the display unit 21, the tag name is displayed in the item Q5 of the tag name in the preview image display area FR2 on the setting screen G5. To display. That is, the tag name is displayed as information about the original when the preview is displayed.

By displaying the tag name for such a property or a list of files, it is possible to easily confirm the classification of the image of the executed manuscript.

In the above embodiment, one embodiment of the present invention is described using an image processing device (multifunction device), but this is only an example, and other images such as a copier, a printer, and a facsimile machine. It may be a processing device.

Further, the configuration and processing of the above-described embodiment described with reference to FIGS. 1 to 15 are merely examples of the present invention, and the present invention is not intended to be limited to the configuration and processing.

10 Image processing device 11 Image reading unit 12 Image forming unit 21 Display unit 22 Operation unit 23 Touch panel 28 Storage unit 29 Control unit 31 Control unit 91 Network interface unit

Claims (5)

The image acquisition unit that acquires the image data of the manuscript and
The operation unit where settings and instructions are input by user operation,
A control unit for setting a training mode or a classification mode according to an instruction input to the operation unit is provided.
The control unit
When the training mode setting is input by operating the operation unit, the characteristics of the image of the manuscript whose image data has been acquired by the image acquisition unit are extracted, and the data indicating the characteristics of the image of the manuscript is obtained. , Collected as learning data to determine the type of the manuscript,
When the classification mode is set by the operation to the operation unit, the feature of the image of the manuscript whose image data has been acquired by the image acquisition unit is extracted, and the feature of the image of the manuscript and the learning data are used. An image processing device that discriminates the type of the manuscript based on the above and adds information indicating the discriminated type to the image data of the manuscript. When a different document type is input by operating the operation unit after determining the type of the document in the classification mode, the control unit indicates the input type instead of the determined type. The image processing apparatus according to claim 1, wherein the information is added to the image data of the manuscript, and the learning data used for the determination is stored in association with the input different types of manuscripts. The image processing device according to claim 1 or 2, wherein the control unit extracts the hue, contrast, number of colors, or density of the image data as a feature of the image of the original document. With a further display
The image according to any one of claims 1 to 3, wherein the control unit associates the type of the original document determined in the classification mode with the image data of the acquired original document and displays it on the display unit. Processing equipment. The image acquisition step to acquire the image data of the manuscript and
Operation steps that accept user operations on the operation unit,
When the training mode is set in the operation step, the characteristics of the image of the manuscript whose image data has been acquired in the image acquisition step are extracted, and the data indicating the characteristics of the image of the manuscript is used to obtain the type of the manuscript. Collected as learning data for discrimination, when the classification mode is set in the operation step, the characteristics of the image of the document for which the image data was acquired in the image acquisition step are extracted, and the image of the document is An image processing method including a control step of discriminating the type of the manuscript based on the features and the learning data and adding information indicating the discriminated type to the image data of the manuscript.