Image processing device

Abstract

To easily mask only a region corresponding to a user-specified item among image data obtained by reading a document.SOLUTION: An image processing device comprises: an image reading unit; a control unit that generates masked data in which at least a part of a region of a target item present in image data is masked; and an operation display unit. The operation display unit receives an item designation operation. The control unit recognizes an item designated through the item designation operation as s target item, and performs, as masking processing, processing for masking at least a region of an item value within a region of the target item present in the image data so as to generate masked data.SELECTED DRAWING: Figure 14

Description

[Technical field]

[0001] The present invention relates to an image processing device. [Background technology]

[0002] Conventionally, there has been known an image processing device that reads an identification card as an original and masks an area of ​​personal information in the image data obtained by the reading. Such an image processing device is disclosed in, for example, Patent Document 1. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2021-197682 A Summary of the Invention [Problem to be solved by the invention]

[0004] For example, personal information written on an ID card is classified into multiple items. The items include name and address. With such an ID card, it may be desirable to mask only the areas of the image data obtained by scanning that correspond to the specified items. Note that the areas to be masked differ depending on the user.

[0005] Here, if only a specific type of ID is to be targeted, the area to be masked (in other words, the position in the image data of the ID) can be set in advance. In this way, if the ID read is of the specific type, only the area of ​​the image data of the ID that corresponds to the specified item is masked.

[0006] However, when reading and masking an identification card of a type different from the predetermined type, it is necessary to set the area to be masked each time, which is troublesome and inconvenient for the user.

[0007] The present invention has been made to solve the above-mentioned problems, and aims to provide an image processing device that can easily mask only the areas of image data obtained by reading a document that correspond to items specified by the user. [Means for solving the problem]

[0008] In order to achieve the above object, an image processing device according to one aspect of the present invention includes an image reading unit that reads a document on which multiple sets of item names and item values ​​of items related to personal information are written, a control unit that recognizes target items that are items to be masked and performs a masking process on image data obtained by reading the document with the image reading unit to generate masked data in which at least a portion of the area of ​​the target items present in the image data is masked, and an operation display unit that displays information and accepts operations. The operation display unit accepts an item designation operation for designating any one of the items. The control unit recognizes the item designated by the item designation operation as the target item and performs a process of masking at least the area of ​​the item value of the area of ​​the target item present in the image data to generate masked data as the masking process. Effect of the Invention

[0009] In the configuration of the present invention, it is possible to easily mask only the areas of image data obtained by scanning an original that correspond to items designated by the user. [Brief description of the drawings]

[0010] [Figure 1] FIG. 1 is a schematic diagram of a multifunction peripheral according to an embodiment. [Diagram 2] FIG. 1 is a block diagram of a multifunction peripheral according to an embodiment. [Diagram 3] 1 is a diagram illustrating an example of an identification card that can be read by a multifunction peripheral according to an embodiment; [Figure 4] 10 is a flowchart illustrating the flow of a masking job executed by the multifunction peripheral according to an embodiment. [Diagram 5]1 is a diagram showing a first area in image data obtained by reading an identification card using a multifunction peripheral according to an embodiment of the present invention; [Figure 6] 11 is a diagram showing a second area in image data obtained by reading an identification card using the multifunction peripheral of the embodiment; FIG. [Figure 7] FIG. 11 is a diagram illustrating masked data generated in a masking job by the multifunction peripheral of the embodiment. [Figure 8] 11 is a conceptual diagram of dictionary data used in a masking job by the multifunction peripheral of an embodiment. FIG. [Figure 9] 10 is a flowchart showing the flow of an area determination process performed in the multifunction peripheral of an embodiment. [Figure 10] 11 is a flowchart showing a flow of a first abnormality countermeasure process performed in the multifunction peripheral of an embodiment. [Figure 11] 13 is a flowchart showing a flow of second and third abnormality countermeasure processes performed in the multifunction peripheral of an embodiment. [Figure 12] 13 is a flowchart showing a flow of a second abnormality countermeasure process performed in the multifunction peripheral of an embodiment. [Figure 13] 13 is a flowchart showing a flow of a third abnormality countermeasure process performed in the multifunction peripheral of an embodiment. [Figure 14] 10 is a flowchart showing a flow of a process performed when a masking job setting is accepted by the multifunction peripheral of an embodiment. [Figure 15] FIG. 13 illustrates a reception screen displayed by the multifunction peripheral of the embodiment. [Figure 16] FIG. 13 illustrates a registration screen displayed by the multifunction peripheral according to an embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0011] <Configuration of multifunction device> An image processing apparatus according to an embodiment of the present invention will be described below with reference to FIGS. 1 to 16, taking as an example a multifunction peripheral 100 having multiple functions such as a scanning function, a printing function, and a transmission function.

[0012] As shown in FIG. 1, multifunction device 100 (corresponding to an "image processing device") includes a printing unit 1. Printing unit 1 constitutes the main body of multifunction device 100. Printing unit 1 prints an image on a sheet S. The printing method of printing unit 1 is an electrophotographic method. However, this is not limited to this. The printing method of printing unit 1 may also be an inkjet method.

[0013] The printing unit 1 forms an image based on image data input to the multifunction device 100. The printing unit 1 also transports a sheet S along a sheet transport path. The printing unit 1 prints an image on the sheet S being transported. In FIG. 1, the sheet transport path is indicated by a dashed line.

[0014] The printing unit 1 includes a paper feed roller 11. The paper feed roller 11 comes into contact with a sheet S stored in a sheet cassette CA and rotates in this state, thereby feeding the sheet S from the sheet cassette CA to a sheet transport path.

[0015] The printing unit 1 includes an image forming unit 12. The image forming unit 12 includes a photoreceptor drum 12a and a transfer roller 12b. The photoreceptor drum 12a carries a toner image on its peripheral surface. The transfer roller 12b is in pressure contact with the photoreceptor drum 12a and forms a transfer nip between the photoreceptor drum 12a and the transfer roller 12b. The transfer roller 12b rotates together with the photoreceptor drum 12a. The image forming unit 12 transfers the toner image onto the sheet S while transporting the sheet S that has entered the transfer nip.

[0016] Although not shown, the image forming unit 12 further includes a charging device, an exposure device, and a developing device. The charging device charges the peripheral surface of the photoconductor drum 12a. The exposure device forms an electrostatic latent image on the peripheral surface of the photoconductor drum 12a. The developing device develops the electrostatic latent image on the peripheral surface of the photoconductor drum 12a into a toner image.

[0017] The printing unit 1 includes a fixing unit 13. The fixing unit 13 includes a heating roller 13a and a pressure roller 13b. The heating roller 13a has a built-in heater (not shown). The pressure roller 13b is pressed against the heating roller 13a to form a fixing nip between the heating roller 13a and the pressure roller 13b. The pressure roller 13b rotates together with the heating roller 13a. The fixing unit 13 fixes the toner image transferred to the sheet S to the sheet S while transporting the sheet S that has entered the fixing nip. The sheet S that has exited the fixing nip is discharged to an output tray ET.

[0018] The multifunction device 100 also includes an image reading unit 2. The image reading unit 2 is disposed on the upper portion of the main body of the multifunction device 100. In a job involving reading an original D, the original D is set in the image reading unit 2. The image reading unit 2 reads the original D set in the image reading unit 2, and generates image data of the read original D.

[0019] The image reading unit 2 includes contact glasses G1 and G2. The contact glasses G1 and G2 are disposed in a housing RH of the image reading unit 2. The housing RH has an opening on the top surface. The contact glasses G1 and G2 are attached to the openings on the top surface of the housing RH.

[0020] The image reading unit 2 includes a document transport device DP. The document transport device DP is attached to the housing RH. When viewed from the front of the multifunction device 100, the document transport device DP rotates with the rear portion as a fulcrum, swinging the front portion up and down. The document transport device DP opens and closes with respect to the top surface of the housing RH.

[0021] The document transport device DP has a set tray ST on which a document D is set. The document transport device DP transports the document D set on the set tray ST onto a contact glass G1.

[0022] In the transport reading mode, the user sets the document D on the set tray ST. Then, the document D that is automatically transported onto the contact glass G1 by the document transport device DP (in other words, the document D passing over the contact glass G1) is read. On the other hand, in the placement reading mode, the user sets the document D on the contact glass G2, and the document D on the contact glass G2 is read.

[0023] The image reading unit 2 includes a light source 21, an image sensor 22, a mirror 23, and a lens 24. The light source 21, the image sensor 22, the mirror 23, and the lens 24 are disposed inside a housing RH. The image reading unit 2 performs a scanning operation in which light is irradiated from the light source 21 toward the contact glass G1 or G2, and the image sensor 22 performs photoelectric conversion.

[0024] The light source 21 has a plurality of LED elements. The plurality of LED elements are arranged in a line in the main scanning direction (a direction perpendicular to the paper surface of FIG. 1). The image sensor 22 has a plurality of photoelectric conversion elements arranged in the main scanning direction. The mirror 23 reflects light toward the lens 24. The lens 24 collects the light reflected by the mirror 23 and guides it to the image sensor 22.

[0025] The light source 21 and the mirror 23 are mounted on a carriage 25 that is movable in a sub-scanning direction (left and right direction in FIG. 1) perpendicular to the main scanning direction. When the carriage 25 moves in the sub-scanning direction, the reading line of the image reading unit 2 moves in the sub-scanning direction.

[0026] As shown in FIG. 2, the multifunction device 100 also includes an operation display unit 3. The operation display unit 3 is an operation panel having a touch screen. The operation display unit 3 displays software buttons, messages, and the like on the touch screen. The operation display unit 3 is also provided with a plurality of hardware buttons. The operation display unit 3 receives operations from a user. The user can configure the multifunction device 100 via the operation display unit 3 with respect to various jobs, such as a masking job, which will be described later.

[0027] The multifunction device 100 includes a control unit 10. The control unit 10 includes a CPU, an ASIC, a memory, and the like. The control unit 10 includes an image processing circuit. The control unit 10 performs various image processing operations on image data. The control unit 10 also controls the printing of an image on a sheet S by the printing unit 1, and controls the reading of an original D by the image reading unit 2.

[0028] The control unit 10 also controls the operation display unit 3. Specifically, the control unit 10 controls the display operation of the touch screen. The control unit 10 detects operations on software buttons and hardware buttons. The control unit 10 sets a job based on an operation received by the operation display unit 3 from a user.

[0029] The multifunction device 100 includes a storage unit 101. The storage unit 101 is a non-volatile storage device. A HDD, an SSD, or the like may be used as the storage unit 101. The storage unit 101 is connected to the control unit 10. The control unit 10 writes information to the storage unit 101 and reads information from the storage unit 101.

[0030] The storage unit 101 stores predetermined information in advance. For example, the storage unit 101 stores a character recognition program in advance. The control unit 10 performs character recognition processing such as OCR (Optical Character Recognition) processing based on the character recognition program. The control unit 10 subjects the image data obtained by reading the document D by the image reading unit 2 to the character recognition processing.

[0031] The multifunction device 100 includes a communication unit 102. The communication unit 102 is an interface for communicatively connecting an external device to the multifunction device 100. The communication unit 102 includes a communication circuit, a communication memory, a communication connector, and the like. The communication unit 102 is connected to the control unit 10. The control unit 10 uses the communication unit 102 to transmit and receive data to and from the external device.

[0032] The communication unit 102 is communicably connected to an external device via a network NT such as a LAN or the Internet. Although not shown, the communication unit 102 may be directly connected to an external device via a communication cable. An external device connected to the communication unit 102 is, for example, a personal computer 1000 (hereinafter referred to as PC 1000) used by a user of the multifunction device 100. An external device other than the PC 1000 may be communicably connected to the multifunction device 100. By connecting the PC 1000 to the multifunction device 100, image data of the original D obtained by reading the original D with the image reading unit 2 can be transmitted to the PC 1000. As a result, the image data of the original D can be stored in the PC 1000.

[0033] <Masking job overview> When entering into a contract for goods or services, the person who wishes to enter into the contract may be required to register personal information. This is just one example, and there are various other cases in which registration of personal information is required. Examples of personal information include name, address, and date of birth.

[0034] A personal information registration task is performed by a user of the multifunction device 100. When registering personal information, the user uses the scanning function of the multifunction device 100 to read an identification card. There are various types of identification cards that can be used, which differ depending on the country and field. Examples of identification cards include a driver's license, an insurance card, a student ID card, and a passport.

[0035] An example of an ID card is shown in FIG. 3. Personal information is classified into multiple items and written on the ID card. In other words, multiple sets of item names and item values ​​for items related to personal information are written on the ID card. In the example shown in FIG. 3, the character string "Name" corresponds to the item name, and the character string "aaaa" corresponds to the item value of the item corresponding to the item name "Name". The character string "Address" corresponds to the item name, and the character string "bbbb" corresponds to the item value of the item corresponding to the item name "Address". The character string "Date of Birth" corresponds to the item name, and the character string "cccc" corresponds to the item value of the item corresponding to the item name "Date of Birth".

[0036] When the ID shown in Figure 3 is read, the image data obtained by reading the ID contains a character area including the string "Name" indicating the item name and a character area including the string "aaaa" indicating the item value. A character area including the string "Address" indicating the item name and a character area including the string "bbbb" indicating the item value. A character area including the string "Date of Birth" indicating the item name and a character area including the string "cccc" indicating the item value.

[0037] In the following explanation, when it is necessary to distinguish between multiple character areas, the character area of ​​the item name "Name" is given the code C11, and the character area of ​​the item value "aaaa" is given the code C12. The character area of ​​the item name "Address" is given the code C21, and the character area of ​​the item value "bbbb" is given the code C22. The character area of ​​the item name "Date of Birth" is given the code C31, and the character area of ​​the item value "cccc" is given the code C32.

[0038] After the multifunction device 100 reads the identification card, an image based on the image data of the identification card obtained by the reading (i.e., the personal information written on the identification card) is printed on a sheet S. The sheet S on which the personal information is printed is then stored. In this manner, the personal information is registered. Note that the image data (i.e., electronic data) of the identification card itself may also be stored.

[0039] Here, in some cases, it may be necessary to mask some of the personal information. For example, suppose that an ID card has a column for the date of birth, but the date of birth is not required in a contract for goods and services. In this example, it may be necessary to mask the personal information related to the date of birth. Also, for example, in some countries, an ID card has a column for religion, but it is stipulated that personal information related to religion should not be recorded. In this example, it may be necessary to mask the personal information related to religion.

[0040] For example, it has been common practice to manually mask out personal information by blacking out the personal information to be masked, but it is troublesome for users to manually mask out some personal information.

[0041] For this reason, a masking function is installed in the multifunction device 100. In other words, the multifunction device 100 is capable of executing a job related to the masking function (hereinafter, simply referred to as a "masking job").

[0042] By using the masking function, it is possible to obtain masked data, which is image data in which some of the personal information on the identification card has been masked. An image based on the masked data can be printed on the sheet S, or the masked data can be sent to the PC 1000 and stored in the PC 1000.

[0043] When a masking job is executed, the masking job is set. In the masking job setting, personal information to be masked can be arbitrarily specified. The masking job setting will be described in detail later.

[0044] After setting the masking job, the user places an identification card as the document D in the image reading unit 2. In this state, the user performs a start operation for the masking job on the operation display unit 3. When it is detected that the start operation has been performed on the operation display unit 3, the control unit 10 starts the masking job.

[0045] The flow of a masking job will be described below with reference to the flowchart shown in Fig. 4. The flow shown in Fig. 4 starts when the control unit 10 detects a start operation of a masking job on the operation and display unit 3.

[0046] Before starting a masking job, the user sets up the masking job. In setting up the masking job, a target item that corresponds to the item value to be masked is specified. When executing the masking job, the control unit 10 recognizes the target item specified by the user.

[0047] In step #1, the control unit 10 causes the image reading unit 2 to read an identification card as the document D. The image reading unit 2 reads the identification card and generates image data of the read identification card (i.e., scan data of the identification card). The control unit 10 acquires the image data of the identification card obtained by the image reading unit 2 reading the identification card.

[0048] In step #2, the control unit 10 performs a first region detection process using a first learning model obtained by machine learning. The control unit 10 performs the first region detection process by detecting a first region that is predicted to individually include either an item name region or an item value region from the image data of the identification card.

[0049] The first learning model for the first area detection process is a learning model that is trained to detect the first area from image data obtained by reading an identification card by the image reading unit 2. The first learning model is a trained model and is stored in the storage unit 101 in advance.

[0050] An identification card usually has a plurality of pairs of an item name and an item value. Therefore, a plurality of first areas are detected in the first area detection process. For this reason, if no first area is detected or only one first area is detected as a result of the first area detection process, the masking job may be stopped and a message may be displayed on the operation display unit 3 to prompt the user to check the document D set in the image reading unit 2.

[0051] In the ID shown in Fig. 3, the character area C11 of the item name "Name", the character area C12 of the item value "aaaa", the character area C21 of the item name "Address", the character area C22 of the item value "bbbb", the character area C31 of the item name "Date of Birth", and the character area C32 of the item value "cccc" are each detected individually as the first area. The first areas are clearly shown in Fig. 5.

[0052] In step #3, the control unit 10 performs a second region detection process using a second learning model obtained by machine learning. The control unit 10 performs the second region detection process by detecting a second region that is predicted to include both the region of the item name and the region of the item value that correspond to the same item from the image data of the identification card.

[0053] The second learning model for the second area detection process is a learning model that is trained to detect the second area from image data obtained by reading an identification card by the image reading unit 2. The second learning model is a trained model and is stored in the memory unit 101 in advance.

[0054] If an identification card has been read, at least one second area is detected as a result of the second area detection process. If no second area is detected in the second area detection process, there is a possibility that an object other than an identification card has been read. Therefore, if no second area is detected in the second area detection process, the masking job may be stopped, and a message may be displayed on the operation display unit 3 to prompt the user to check the document D set in the image reading unit 2.

[0055] In the example shown in Fig. 5, the regions C10, C20, and C30 shown in Fig. 6 are detected as the second region. Specifically, a single region C10 including the character region C11 of the item name "Name" and the character region C12 of the item value "aaaa" is detected as the second region. A single region C20 including the character region C21 of the item name "Address" and the character region C22 of the item value "bbbb" is detected as the second region. A single region C30 including the character region C31 of the item name "Date of Birth" and the character region C32 of the item value "cccc" is detected as the second region.

[0056] In step #4, control unit 10 performs an inclusion region detection process for each of the multiple first regions detected in the first region detection process. Each of the multiple first regions is subjected to the inclusion region detection process once. Control unit 10 performs the inclusion region detection process on one of the first regions as the processing target, and when the inclusion region detection process is completed, it performs the inclusion region detection process on another first region that has not yet been processed as the new processing target.

[0057] The control unit 10 performs a process of detecting a second region that includes the first region to be processed as an inclusion region detection process. The control unit 10 detects the second region that includes the first region to be processed by detecting a second region having an image portion whose image similarity with the first region to be processed is equal to or greater than a threshold value for the inclusion region detection process. The control unit 10 determines that the first region to be processed is included in the second region having an image portion whose image similarity with the first region to be processed is equal to or greater than a threshold value for the inclusion region detection process. Note that character recognition process has not been performed at this point. That is, the image similarity is the similarity between image data, and not the similarity between character strings (i.e., between text data). The control unit 10 performs the inclusion region detection process, for example, using a known pattern matching technique. The threshold value for the inclusion region detection process is not particularly limited, but is 70% or more.

[0058] In the example shown in FIG. 5 and FIG. 6, the second region C10 has an image portion where the image similarity with the first region C11 (region with the item name "Name") is equal to or greater than a threshold, and also has an image portion where the image similarity with the first region C12 (region with the item value "aaaa") is equal to or greater than a threshold. The second region C20 has an image portion where the image similarity with the first region C21 (region with the item name "Address") is equal to or greater than a threshold, and also has an image portion where the image similarity with the first region C22 (region with the item value "bbbb") is equal to or greater than a threshold. The second region C30 has an image portion where the image similarity with the first region C31 (region with the item name "Date of Birth") is equal to or greater than a threshold, and also has a portion where the image similarity with the first region C32 (region with the item value "cccc") is equal to or greater than a threshold. As a result, the first regions C11 and C12 are determined to be included in the second region C10, the first regions C21 and C22 are determined to be included in the second region C20, and the first regions C31 and C32 are determined to be included in the second region C30.

[0059] In step #5, the control unit 10 performs a region linking process. Specifically, the control unit 10 links a plurality of first regions included in the same second region to each other.

[0060] 5 and 6, first regions C11 and C12 included in second region C10 are linked to each other. First regions C21 and C22 included in second region C20 are linked to each other. First regions C31 and C32 included in second region C30 are linked to each other.

[0061] In step #6, the control unit 10 performs an area determination process. By performing the area determination process, the control unit 10 recognizes which of the multiple first areas linked to each other is an area for item names and which is an area for item values. In other words, by performing the area determination process, the control unit 10 distinguishes between the areas for item names and the areas for item values.

[0062] 5 and 6, in the second region C10, the first region C11 is determined to be an item name region, and the first region C12 is determined to be an item value region. In the second region C20, the first region C21 is determined to be an item name region, and the first region C22 is determined to be an item value region. In the second region C30, the first region C31 is determined to be an item name region, and the first region C32 is determined to be an item value region.

[0063] The control unit 10 recognizes the area of ​​the target item by performing an area determination process. The area of ​​the target item is a second area that includes both the first area corresponding to the item name of the target item and the first area corresponding to the item value of the target item. The area determination process will be described in detail later. Note that the area of ​​the target item may be recognized using other methods.

[0064] In step #7, the control unit 10 performs a masking process to mask at least a part of the area of ​​the target item present in the image data of the identification card. By performing the masking process, the control unit 10 generates masked data in which at least a part of the area of ​​the target item present in the image data of the identification card is masked.

[0065] The control unit 10 masks the first region associated with the region of the item name of the target item (i.e., the region of the item value of the target item). Alternatively, the control unit 10 masks both the region of the item name and the region of the item value of the target item. Substantially the entire region of the second region corresponding to the target item may be masked.

[0066] An example of masked data is shown in Fig. 7. The masked data shown in Fig. 7 is generated when the target item is the date of birth. That is, at least the item value "cccc" corresponding to the item name "Date of Birth" is masked. Although not shown, both the item name "Date of Birth" and the item value "cccc" may be masked.

[0067] In step #8, the control unit 10 causes the output unit to perform output processing of the masked data. For example, in the masking job settings, the output method of the masked data can be selected. The output methods include printing and transmission.

[0068] When printing is selected as the output method, the control unit 10 causes the printing unit 1 to print (in other words, output) an image based on the masked data onto a sheet S. In this case, the printing unit 1 corresponds to the "output unit", and the output destination is the sheet S.

[0069] When transmission is selected as the output method, the control unit 10 causes the communication unit 102 to transmit (in other words, output) the masked data to the PC 1000. The masked data may be converted to PDF data and transmitted to the PC 1000. By transmitting the masked data to the PC 1000, the masked data can be stored in the PC 1000. In this case, the communication unit 102 corresponds to the "output unit" and the output destination is the PC 1000.

[0070] In this embodiment, a first region detection process, a second region detection process, an inclusion region detection process, and a region linking process are performed. This allows each region of the item name and item value corresponding to the same item in the image data of the identification card to be linked to each other with high accuracy.

[0071] By linking the fields of the item name and the item value corresponding to the same item in the image data of the identification card to each other, it is possible to easily mask at least a part of the target item. Specifically, it is sufficient to mask the field linked to the field of the item name of the target item. This allows the item value of the target item to be masked. In this configuration, it is possible to easily mask only the fields corresponding to the user-specified items in the image data obtained by reading the document D (identification card) without transferring the image data of the identification card to a processing device on the cloud.

[0072] If the field of the item value in the image data of the ID card can be masked, the leakage of personal information can be suppressed. Even if the field of the item name of the target item is masked, if the field of the item value of the target item is not masked, the personal information will be leaked. Therefore, it is important to perform a process of linking each field of the item name and the item value corresponding to the same item in the image data of the ID card with each other with high accuracy.

[0073] In addition, in this embodiment, the first area detection process is performed using the first learning model, and the second area detection process is performed using the second learning model. Here, machine learning requires a huge amount of calculation and a huge memory capacity. For this reason, the process related to machine learning is often performed by a processing device on the cloud. However, in this case, image data of the identification card needs to be transferred to the processing device on the cloud, which is not preferable because there is a possibility of personal information being leaked.

[0074] Therefore, in this embodiment, a trained first learning model is used, and a trained second learning model is used. The first learning model and the second learning model are pre-stored in the storage unit 101, respectively. This allows the first area detection process and the second area detection process to be performed within the multifunction device 100 without increasing the memory capacity within the multifunction device 100. In other words, it is not necessary to transfer image data of the identification card to a processing device on the cloud. As a result, leakage of personal information can be suppressed.

[0075] <Area determination process> The control unit 10 performs an area determination process for determining whether each of a plurality of first areas (i.e., character areas including character strings) present in the image data of the identification card is an area for an item name or an area for an item value. In the area determination process, dictionary data DD is used. The dictionary data DD is pre-stored in the storage unit 101 (see FIG. 2).

[0076] A conceptual diagram of the dictionary data DD is shown in FIG. 8. The dictionary data DD is data in which multiple different similar item names are predefined for one type of item. The dictionary data DD is created in advance for each of the multiple types of items. The memory unit 101 pre-stores multiple dictionary data DD corresponding to the multiple types of items, respectively. The multiple dictionary data DD are created in advance by the manufacturer of the multifunction device 100 and pre-stored in the memory unit 101. In FIG. 8, items are indicated by A, B.... Similar item names corresponding to item A are indicated by a1, a2, a3.... Similar item names corresponding to item B are indicated by b1, b2, b3....

[0077] Taking an identification number as an example of an item, one ID card may use the character string "ID" as the item name, another ID card may use the character string "Number" as the item name, and yet another ID card may use the character string "Num" as the item name. Although the character strings given here are different from one another, they all correspond to the same item. Thus, in the dictionary data DD corresponding to the identification number as an item, the character string "ID", the character string "Number", the character string "Num", and the like are predefined as similar item names.

[0078] The flow of the area determination process will be described below with reference to the flowchart shown in Fig. 9. The flow shown in Fig. 9 starts when the area linking process by the control unit 10 is completed.

[0079] In step #11, the control unit 10 performs character recognition processing on the image data of the identification card obtained by the image reading unit 2 reading the identification card (i.e., the processing of step #1 shown in FIG. 4). The control unit 10 performs OCR processing on the image data of the identification card. In this way, the control unit 10 extracts character strings from each of the multiple first areas present in the image data of the identification card. The control unit 10 recognizes the character strings in each of the multiple first areas. Note that the first areas contain character strings indicating item names or character strings indicating item values.

[0080] In step #12, the control unit 10 performs preprocessing on the multiple character strings extracted by the character recognition process. The process performed as the preprocessing is not particularly limited. For example, the control unit 10 performs a process to unify uppercase and lowercase letters in the character strings as the preprocessing. This process makes it impossible to distinguish between uppercase and lowercase letters in the character strings. In addition, for example, the control unit 10 performs a process to remove spaces and predetermined symbols as the preprocessing.

[0081] After the process of step #12, the control unit 10 performs an item name area determination process. When performing the item name area determination process, the control unit 10 sets one of the multiple first areas detected in the first area detection process as a processing target.

[0082] Then, the control unit 10 performs a similarity calculation process (the process of step #13) as one process of the item name region determination process. In step #13, the control unit 10 obtains the similarity between each character string of all similar item names defined in the multiple dictionary data DD and the character string of the first region to be processed. Hereinafter, the similarity between character strings is referred to as character string similarity, and is distinguished from image similarity.

[0083] When performing the similarity calculation process, the control unit 10 selects one of the multiple dictionary data DD (here, the selected dictionary data DD is referred to as target dictionary data DD). The control unit 10 also selects one of the multiple similar item names defined in the target dictionary data DD (here, the selected similar item name is referred to as target similar item name).

[0084] Then, the control unit 10 calculates the similarity between the character string in the first region to be processed and the character string of the target similar item name. For example, the control unit 10 can calculate the similarity between the character string in the first region to be processed and the character string of the target similar item name by using the following formula (1).

number

[0085] In formula (1), S1 is one character string and S2 is the other character string. For example, S1 is the character string of the similar item name defined in the dictionary data DD, and S2 is the character string of the first region extracted from the image data of an identification card. len(S1) is the number of characters in the character string S1, and len(S2) is the number of characters in the character string S2. M is the number of characters that match between the character string S1 and the character string S2.

[0086] For example, suppose that character string S1 is "Number" and character string S2 is "Nombern." Even if "Number" is written on the identification card, it may be recognized as "Nombern" due to a misrecognition in the character recognition process.

[0087] In this example, len(S1) = 6 and len(S2) = 7. Furthermore, between string S1 and string S2, counting from the first character, the first character "N", the third character "m", the fourth character "b", the fifth character "e", and the sixth character "r" match each other. This results in M ​​= 5.

[0088] In this example, the similarity value is 0.76923. If the character strings S1 and S2 are completely different characters (i.e., M=0), the similarity value is 0, and if the character strings S1 and S2 are completely the same characters (i.e., M=len(S1)=len(S2)), the similarity value is 1. That is, in calculating the similarity between the character string in the first region to be processed and the character string of the target similar item name, the higher the similarity, the larger the similarity value, and the lower the similarity, the smaller the similarity value.

[0089] After determining the string similarity between the string in the first region to be processed and the string of the target similar item name, the control unit 10 newly selects an unselected similar item name from among the multiple similar item names defined in the target dictionary data DD. The control unit 10 switches the target similar item name and determines the string similarity between the string in the first region to be processed and the string of the new target similar item name. The control unit 10 determines the string similarity between the string in the first region to be processed for all of the multiple similar item names defined in the target dictionary data DD. Then, the control unit 10 stores the similar item name that has the highest string similarity with the string in the first region to be processed from among the multiple similar item names defined in the target dictionary data DD, in association with the value of the string similarity.

[0090] The control unit 10 also obtains the string similarity between all similar item names defined in the multiple dictionary data DD and the character string in the first region to be processed. That is, the control unit 10 newly selects an unselected dictionary data DD from the multiple dictionary data DD. The control unit 10 switches the target dictionary data DD and obtains the string similarity between all similar item names defined in the new target dictionary data DD and the character string in the first region to be processed. Then, the control unit 10 stores the similar item name having the highest string similarity between the similar item names defined in the target dictionary data DD and the character string in the first region to be processed in association with the value of the string similarity. As a result, the multiple similar item names corresponding to each of the multiple types of items are stored in association with the value of the string similarity. In other words, multiple similarity values ​​(the similarity values ​​are the values ​​of the string similarity) corresponding to each of the multiple types of items are stored.

[0091] The control unit 10 performs a maximum value detection process (the process of step #14) as one process of the item name area determination process. In step #14, the control unit 10 compares the multiple similarity values ​​(the similarity values ​​are the values ​​of character string similarity) calculated and stored in the similarity calculation process with each other. In other words, the control unit 10 compares the multiple similarity values ​​respectively corresponding to the multiple types of items with each other.

[0092] Then, the control unit 10 detects the maximum value of the character string similarity (i.e., the highest similarity value). That is, the control unit 10 obtains the character string similarity between the character string in the first region to be processed and each character string of all similar item names, and detects the maximum value among the obtained similarity values.

[0093] The control unit 10 performs a threshold comparison process (the process of step #15) as one process of the item name region determination process. In step #15, the control unit 10 compares a predetermined threshold for the item name region determination process with the maximum value of the string similarity.

[0094] For example, to set a threshold value for the item name area determination process, an identification card is actually read by the multifunction device 100, and the result of a similarity calculation process for character strings present in the item name area of ​​the image data obtained by the reading (here, the resulting value is referred to as the similarity value of the item name area) is obtained. In addition, the result of a similarity calculation process for character strings present in an area other than the item name area of ​​the image data (here, the resulting value is referred to as the similarity value of the other area) is obtained. Note that multiple similarity values ​​are obtained for the item name area, and multiple similarity values ​​for the other areas are also obtained.

[0095] A first average value, which is the average value of the similarity values ​​of the plurality of item name regions, is calculated, and a second average value, which is the average value of the plurality of other regions, is calculated. Then, the average value of the first average value and the second average value is set as a threshold value for the item name region determination process.

[0096] In step #16, the control unit 10 judges whether the maximum value of the character string similarity is equal to or greater than the threshold value for the item name area determination process. In Fig. 9, the threshold value for the item name area determination process is represented as Th. If the control unit 10 judges that the maximum value of the character string similarity is equal to or greater than the threshold value for the item name area determination process, the process proceeds to step #17.

[0097] In step #17, the control unit 10 determines that the first region to be processed is an area of ​​an item name. In addition, in step #18, the control unit 10 determines an item corresponding to the first region to be processed based on a character string as an item name present in the first region to be processed (i.e., the first region determined to be an area of ​​an item name). Here, the character string in the first region to be processed is defined as a similar item name in one of the dictionary data DDs. Therefore, the control unit 10 recognizes an item corresponding to the dictionary data DD in which the character string in the first region to be processed is defined, and determines that the recognized item is an item corresponding to the first region to be processed.

[0098] In step #16, if the control unit 10 determines that the maximum value of the string similarity is less than the threshold value for the item name region determination process, the process proceeds to step #19. When the process proceeds to step #19, the control unit 10 determines that the first region to be processed is another region (i.e., a region different from the region of the item name).

[0099] Each process from step #13 to step #19 is performed as one process of the item name area determination process. After completing the item name area determination process for a certain first area to be processed, the control unit 10 judges whether or not there remains a first area for which the item name area determination process has not been performed (herein referred to as an unprocessed first area). If there remains an unprocessed first area, the control unit 10 sets the unprocessed first area as a new processing target. In other words, the control unit 10 switches the processing target. Then, the control unit 10 performs the item name area determination process (each process from step #13 to step #19) for the first area of ​​the new processing target.

[0100] In this embodiment, dictionary data DD, in which multiple similar item names are predefined for one type of item, is pre-stored in the storage unit 101 for each of multiple types of items. Then, the item name area determination process is performed using the dictionary data DD. This makes it possible to prevent the occurrence of a problem in which, when an item with multiple corresponding item names is to be masked, the item to be masked is properly masked on an identification card on which a certain item name is written among the multiple item names, while the item to be masked is not masked on an identification card on which another item name is written (in other words, the item to be masked is not detected).

[0101] For example, suppose that an identification number as an item is to be masked. In this case, in this embodiment, the identification number is properly masked in both the identification card with the item name "ID" and the identification card with the item name "Num". On the other hand, in the conventional configuration, the identification number is properly masked in the identification card with the item name "ID", but the identification number is not masked in the identification card with the item name "Num".

[0102] To prevent such inconveniences, a large dictionary is required. Also, when using machine learning models, the model size becomes large. However, since memory capacity is limited, processing is performed on the cloud.

[0103] In this embodiment, by using the dictionary data DD, processing on the cloud is not required. In other words, there is no need to transfer image data of the identification card (i.e., personal information) to a processing device on the cloud. This makes it possible to prevent personal information from leaking. In other words, in this embodiment, only the areas corresponding to the items specified by the user can be easily masked out of the image data obtained by reading the document D (identification card) without transferring the image data of the identification card to a processing device on the cloud.

[0104] Furthermore, in this embodiment, the string similarity between the string in the first region to be processed and each of the strings of all similar item names is calculated, and if the highest string similarity calculated is equal to or greater than a threshold value, the first region to be processed is determined to be an item name region. Therefore, even if misrecognition occurs in the character recognition process, the item name region can be detected with high accuracy.

[0105] <Anomaly Countermeasures> First, the first abnormality countermeasure process will be described.

[0106] Normally, the multiple items of personal information written on an ID are different from each other. For example, it is highly unlikely that a single ID will have multiple items of "name". However, if the accuracy of character recognition processing on the image data of an ID is low, multiple item names (character strings thereof) each corresponding to the same item may be extracted from the image data of the ID. If there is dirt or discoloration on any part of the ID, or if the resolution used to read the ID is low, the accuracy of the character recognition processing will decrease.

[0107] In order to prevent such inconvenience, the control unit 10 performs a first abnormality countermeasure process. Specifically, when the character strings of the multiple first regions determined to be areas of item names correspond to the same first item, the control unit 10 determines the first region that includes the character string with the highest maximum value among the multiple first regions as the region corresponding to the first item, and determines the remaining first regions as not regions corresponding to the first item. In other words, the control unit 10 corrects the result of the item name region determination process.

[0108] The flow of the first abnormality countermeasure processing will be described below with reference to the flowchart shown in Fig. 10. The flow shown in Fig. 10 starts when the item name area determination processing (i.e., processing following the flow shown in Fig. 9) by the control unit 10 is completed. After completing the item name area determination processing, the control unit 10 continues to perform the first abnormality countermeasure processing on the first area to be processed.

[0109] In step #21, the control unit 10 judges whether the first area to be processed is determined to be an area of ​​an item name. If the control unit 10 judges that the first area to be processed is an area of ​​an item name, the process proceeds to step #22. If the control unit 10 judges that the first area to be processed is not an area of ​​an item name, the process ends.

[0110] In step #22, the control unit 10 judges whether the item corresponding to the first region to be processed is the same as the item corresponding to another first region. In other words, the control unit 10 judges whether the character strings in each of the first regions determined to be regions of item names correspond to the same item (first item).

[0111] In step #22, if the control unit 10 determines that the item corresponding to the first region to be processed is the same as the item corresponding to the other first region, the process proceeds to step #23. In other words, if the control unit 10 determines that the character strings in each of the multiple first regions determined to be regions of item names correspond to the same item (first item), the process proceeds to step #23. If the control unit 10 determines that the item corresponding to the first region to be processed is different from the item corresponding to the other first region, the process ends. Hereinafter, the other first region having the same corresponding item as the first region to be processed is referred to as the first region to be compared.

[0112] In step #23, the control unit 10 recognizes the maximum value found in the maximum value detection process (i.e., the process of step #14 shown in FIG. 9) for each of the first region to be processed and the first region to be compared. Then, the control unit 10 compares the maximum values ​​of the first region to be processed and the first region to be compared with each other. As a result, the maximum values ​​of the first region to be processed and the first region to be compared with each other. For example, the control unit 10 determines whether the maximum value M1 of the first region to be processed is equal to or greater than the maximum value M2 of the first region to be compared.

[0113] In step #23, if the control unit 10 determines that the maximum value M1 of the first region to be processed is equal to or greater than the maximum value M2 of the first region to be compared, the process proceeds to step #24. If the control unit 10 determines that the maximum value M1 of the first region to be processed is less than the maximum value M2 of the first region to be compared, the process proceeds to step #25.

[0114] When the process proceeds to step #24, the control unit 10 does not change the determination result that the first region to be processed is the region corresponding to the first item, but changes the determination result for the first region to be compared. Specifically, the control unit 10 recognizes the second item, which is the item corresponding to the similar item name having the second highest string similarity with the string of the first region to be compared, and determines that the first region to be compared is the region corresponding to the second item.

[0115] When the process proceeds to step #25, the control unit 10 does not change the determination result that the first region to be compared is the region corresponding to the first item, but changes the determination result for the first region to be processed. Specifically, the control unit 10 recognizes the second item, which is the item corresponding to the similar item name having the second highest string similarity with the string of the first region to be processed, and determines that the first region to be processed is the region corresponding to the second item.

[0116] In other words, the following processes are carried out in steps #23 to #25.

[0117] When the character strings of the first regions determined to be regions of an item name correspond to the same first item, the control unit 10 determines, among the first regions, the first region containing the character string with the highest maximum value as the region corresponding to the first item. In addition, the control unit 10 recognizes, among the first regions, a second item that is an item corresponding to a similar item name having the second highest character string similarity with the character string of the first region not determined to be a region corresponding to the first item, and determines the first region not determined to be a region corresponding to the first item as a region corresponding to the second item.

[0118] Next, the second abnormality countermeasure processing and the third abnormality countermeasure processing will be described.

[0119] An ID card has multiple item columns. One item name is written in each item column, and multiple item names are rarely written in one item column. That is, usually, two or more of the multiple first regions included in the same second region are not determined as item name regions. Also, usually, one of the multiple first regions included in the same second region is determined as an item name region. However, if the accuracy of the character recognition process for the image data of the ID card is low, two or more of the multiple first regions included in the same second region may be determined as item name regions, or none of the multiple first regions may be determined as item name regions.

[0120] Therefore, the control unit 10 performs a second abnormality countermeasure process and a third abnormality countermeasure process. Here, a plurality of first regions included in the same second region are linked to each other. Therefore, the control unit 10 performs the second abnormality countermeasure process or the third abnormality countermeasure process on the plurality of first regions that are linked to each other.

[0121] Hereinafter, the flows of the second abnormality countermeasure processing and the third abnormality countermeasure processing will be described with reference to the flowcharts shown in FIGS.

[0122] First, the control unit 10 performs processing according to the flow shown in Fig. 11. The flow shown in Fig. 11 starts when the area determination processing by the control unit 10 (i.e., processing according to the flow shown in Fig. 9) is completed. After completing the item name area determination processing, the control unit 10 continues to perform processing according to the flow shown in Fig. 11.

[0123] In step #31, the control unit 10 performs a character string acquisition process to acquire a character string in the first region associated with the first region to be processed. Then, in step #32, the control unit 10 judges whether or not a region determination process has been performed on the first region including the character string acquired in the character string acquisition process (i.e., whether or not the region has been determined). If it is determined that the region has been determined, the process proceeds to step #33, and if it is determined that the region has not been determined, the flow ends.

[0124] In step #33, the control unit 10 judges whether the first area to be processed and the first area linked to the first area to be processed are both determined to be areas of item names. In other words, the control unit 10 judges whether two or more of the multiple first areas linked to each other are determined to be areas of item names.

[0125] In step #33, if the control unit 10 determines that two or more of the first regions linked to each other are determined to be regions of item names, the process proceeds to step #34. In step #34, the control unit 10 performs a second abnormality countermeasure process.

[0126] If the control unit 10 determines in step #33 that two or more of the first areas linked to each other have not been determined to be areas of item names, the process proceeds to step #35. In step #35, the control unit 10 determines whether or not the first area to be processed and the first area linked to the first area to be processed have both been determined to be areas different from the area of ​​item names. In other words, the control unit 10 determines whether or not all of the first areas linked to each other have been determined to be areas different from the area of ​​item names.

[0127] In step #35, if the control unit 10 determines that all of the first regions linked to each other are determined to be regions different from the region of the item name, the process proceeds to step #36. In step #36, the control unit 10 performs a third abnormality countermeasure process.

[0128] In step #35, if the control unit 10 does not determine that all of the first regions linked to each other are regions different from the region of the item name, this flow ends. In other words, if only one of the first regions linked to each other is determined to be the region of the item name, neither the second abnormality countermeasure process nor the third abnormality countermeasure process is performed.

[0129] As the second abnormality countermeasure process, a process is performed along the flow shown in Fig. 12. That is, when the control unit 10 determines that two or more of the multiple first areas linked to each other (i.e., multiple first areas included in the same second area) are areas of item names, a process is performed along the flow shown in Fig. 12.

[0130] In step #41, the control unit 10 recognizes the maximum value found in the maximum value detection process (i.e., the process of step #14 in FIG. 9) for each of the multiple first regions linked to each other. In addition, in step #42, the control unit 10 recognizes the highest maximum value.

[0131] Then, in step #43, the control unit 10 determines that the first region containing the character string with the highest maximum value (herein referred to as the first region with the highest maximum value) is an item name region among the multiple first regions linked to each other. Also, in step #44, the control unit 10 determines that the remaining first regions other than the first region with the highest maximum value are regions other than the item name region among the multiple first regions linked to each other.

[0132] Here, when a first area present in the image data of the identification card is determined to be an area of ​​an item name, the control unit 10 stores information indicating that the first area corresponds to the item name. In this configuration, when the second abnormality countermeasure process is performed, for a first area that is re-determined to be an area different from an item name area, the information indicating that the area corresponds to the item name should be deleted. Therefore, in step #45, the control unit 10 deletes the information indicating that the remaining first areas other than the first area with the highest maximum value correspond to the item name.

[0133] As the third abnormality countermeasure process, a process is performed along the flow shown in Fig. 13. That is, when the control unit 10 determines that all of the multiple first areas linked to each other (i.e., multiple first areas included in the same second area) are areas different from the area of ​​the item name, a process is performed along the flow shown in Fig. 13.

[0134] In step #51, the control unit 10 recognizes the maximum value found in the maximum value detection process (i.e., the process of step #14 in FIG. 9) for each of the multiple first regions linked to each other. In addition, in step #52, the control unit 10 recognizes the highest maximum value.

[0135] Then, in step #53, the control unit 10 determines that the first region containing the character string with the highest maximum value (herein referred to as the first region with the highest maximum value) is an item name region among the multiple first regions linked to each other. Also, in step #54, the control unit 10 determines that the remaining first regions other than the first region with the highest maximum value are regions other than the item name region among the multiple first regions linked to each other.

[0136] Here, when the first area present in the image data of the identification card is determined to be an area of ​​an item name, the control unit 10 stores information that the first area corresponds to the item name. In this configuration, when the third abnormality countermeasure process is performed, information that the first area corresponds to the item name should be added to the first area that is re-determined to be an area of ​​an item name by the process. Therefore, in step #55, the control unit 10 adds information that the first area with the highest maximum value corresponds to the item name.

[0137] In this embodiment, by performing the first to third abnormality countermeasure processes, it is possible to prevent the occurrence of a problem in which multiple areas with item names corresponding to the same item are detected. It is also possible to prevent the occurrence of a problem in which an area with an item name is not detected.

[0138] <Masking job settings> In a masking job, the user can set the masking job. The masking job is set via the operation display unit 3. For example, the user can specify an item corresponding to an item value to be masked (that is, a target item).

[0139] Specifically, the operation display unit 3 accepts an item designation operation to designate one of the items. The control unit 10 recognizes the item designated by the item designation operation as a target item. Then, as a masking process, the control unit 10 performs a process of masking at least the area of ​​the item value among the areas of the target item present in the image data of the identification card to generate masked data.

[0140] The flow of processing performed by the control unit 10 when accepting settings for a masking job will be described below with reference to the flowchart shown in Fig. 14. For example, in a masking job, after the image reading unit 2 reads an identification card, and before the masking process starts, an item designation operation is accepted by the operation display unit 3. That is, after the process of step #1 shown in Fig. 4 and before the process of step #7, the flow shown in Fig. 14 starts.

[0141] In step #61, the operation display unit 3 displays a reception screen 31 as shown in FIG. 15. Reception buttons RB are arranged on the reception screen 31. The reception buttons RB include a basic settings button B1 and a custom button B2. The operation display unit 3 accepts an operation on any of the reception buttons RB as an item designation operation. The control unit 10 determines the item designated by the user (i.e., the target item) based on which reception button RB the item designation operation was performed on.

[0142] Specifically, the storage unit 101 stores a masking list in which at least one item is registered. The reception button RB is associated with the masking list. When there are multiple masking lists, multiple reception buttons RB (specifically, the same number as the number of masking lists) are displayed on the reception screen 31. The multiple reception buttons RB are associated with different masking lists.

[0143] In the masking list corresponding to the basic settings button B1, items preselected by the manufacturer are registered. In the masking list corresponding to the custom button B2, items arbitrarily selected by the user are registered. By creating a new masking list, the custom button B2 (i.e., the reception button RB) corresponding to the newly created masking list is displayed on the reception screen 31.

[0144] In order to receive a new masking list creation request from the user, a new creation button NB is displayed on the reception screen 31. The operation display unit 3 receives an operation on the new creation button NB as a new masking list creation request.

[0145] When a request to create a new masking list is received, the operation display unit 3 displays a registration screen 32 as shown in Fig. 16, and receives a registration operation from the user to select an item to be registered. A plurality of items (the item names) are displayed as options on the registration screen 32.

[0146] The operation display unit 3 accepts an operation on the display field of any of the displayed items as a registration operation. For example, if one wishes to register the item "height", the item "height" can be selected as the item to be registered by operating the display field of the item "height". A check mark is added to the display field of the item selected in the registration operation. The selection of the item through the registration operation can be completed by operating the decision button DB in the registration screen 32.

[0147] When the decision button DB is operated, the control unit 10 determines that the acceptance of the registration operation is completed. After the acceptance of the registration operation, the control unit 10 generates a new masking list in which the items selected in the registration operation are registered, and stores the new masking list in the storage unit 101.

[0148] When a new masking list is created and the operation display unit 3 subsequently receives an item designation operation, the control unit 10 causes the operation display unit 3 to display a reception screen 31 on which a reception button RB corresponding to the new masking list is arranged in addition to the reception button RB corresponding to the existing masking list.

[0149] When any of the reception buttons RB is operated, the control unit 10 judges that the operation display unit 3 has accepted an item designation operation. At this time, the control unit 10 judges that the masking list corresponding to the reception button RB on which the item designation operation was performed has been designated. In other words, the item designation operation is an operation for designating any of the masking lists.

[0150] 14, in step #62, the control unit 10 recognizes the reception button RB on which the item designation operation has been performed, i.e., the masking list designated by the item designation operation. Then, the control unit 10 recognizes the item registered in the masking list designated by the item designation operation as the target item.

[0151] In step #63, the control unit 10 performs a masking process. The process of step #63 is the same as the process of step #7 shown in Fig. 4. Note that the control unit 10 performs the masking process after performing each process of steps #2 to #6 shown in Fig. 4.

[0152] In step #64, the control unit 10 causes the operation display unit 3 to display a preview image (not shown) showing the contents of the masked data, and causes the operation display unit 3 to accept an editing operation for editing the masked data. The editing operation is an operation for changing the items to be masked. In other words, the editing operation includes an operation for adding a new item as a masking target, and an operation for removing a masked item from the masking target.

[0153] In step #65, the control unit 10 judges whether or not an editing operation has been performed. If an editing operation has been performed, the process proceeds to step #61. After that, in step #63, the control unit 10 generates new masked data that reflects the editing contents of the editing operation.

[0154] If it is determined in step #65 that no editing operation has been performed, the process proceeds to step #66. In step #66, the control unit 10 causes the output unit to perform output processing of the masked data. The processing in step #66 is the same as the processing in step #8 shown in FIG.

[0155] In this embodiment, after scanning the ID, an item designation operation is accepted to designate the items to be masked (i.e., target items). By performing the item designation operation, at least the area of ​​the item value among the areas of the target items present in the image data of the ID is masked. In other words, it is possible to easily mask only the areas corresponding to the items designated by the user among the image data obtained by reading the document D (ID).

[0156] For example, in the conventional configuration, it is necessary to scan the ID card in advance and register the area to be masked (the position in the image data). In other words, the ID card needs to be pre-registered. In the conventional configuration, if the ID card is pre-registered, the desired area in the image data of the ID card can be masked.

[0157] However, from the user's perspective, the task of pre-registering ID cards is cumbersome. If there are many types of ID cards that are applicable, the ID cards must be pre-registered for each type, which increases the amount of work. In addition, the masking target cannot be changed flexibly. If a user wants to change the masking target, they must re-register the ID card, which is cumbersome.

[0158] On the other hand, in this embodiment, when a certain ID is read, the area of ​​the item specified by the item designation operation in the image data is masked by performing the item designation operation, and when another ID of a different type from the ID is read, the area of ​​the item specified by the item designation operation in the image data is also masked. Also, for example, when the masking target is item A, if it is necessary to further add item B as a masking target (i.e., when it is necessary to change the masking target), it is sufficient to simply designate items A and B by the item designation operation. As a result, the work equivalent to pre-registering an ID is unnecessary, improving user convenience.

[0159] In addition, in this embodiment, the item designation operation is an operation of designating a masking list. As a result, when a user wants to mask multiple items, the user does not need to designate the items to be masked one by one. This further improves user convenience.

[0160] In addition, in this embodiment, a masking list can be newly created, which is convenient for the user because the masking list can be created arbitrarily.

[0161] In addition, in this embodiment, the masked data can be edited, improving user convenience. When editing the masked data, a preview image showing the contents of the masked data is displayed. This allows the masked data to be previewed while being edited, making it possible to efficiently edit the masked data.

[0162] The embodiments disclosed herein are illustrative in all respects and should not be considered as limiting. The scope of the present invention is indicated by the claims, not by the description of the above embodiments, and further includes all modifications within the meaning and scope of the claims. [Explanation of symbols]

[0163] 1 Printing section (output section) 2 Image reading unit 3 Operation display section 10 Control section 100 Multifunction printers (image processing devices) 102 Communication section (output section) 1000 PCs (external equipment) D Manuscript S Seat

Claims

1. an image reading unit that reads a document on which a plurality of sets of item names and item values ​​of items related to personal information are written; a control unit that recognizes a target item that is an item to be masked, and performs a masking process on image data obtained by reading the document by the image reading unit, thereby generating masked data in which at least a portion of an area of ​​the target item present in the image data is masked; An operation display unit that displays information and accepts operations, the operation display unit accepts an item designation operation for designating any one of the items; the control unit recognizes the item specified by the item specifying operation as the target item, and performs, as the masking process, a process of masking at least an area of ​​the item value among areas of the target item present in the image data to generate masked data; A storage unit that stores a masking list in which at least one of the items is registered, the item designation operation is an operation of designating any one of the masking lists, The control unit recognizes the item registered in the masking list specified by the item specifying operation as the target item.

2. the operation display unit accepts a request to create a new masking list, When the new creation request is received, the operation display unit receives a registration operation for selecting the item to be registered; The image processing device according to claim 1 , wherein after the registration operation is accepted, the control unit generates a new masking list in which the item selected in the registration operation is registered, and stores the new masking list in the storage unit.

3. an image reading unit that reads a document on which a plurality of sets of item names and item values ​​of items related to personal information are written; a control unit that recognizes a target item that is an item to be masked, and performs a masking process on image data obtained by reading the document by the image reading unit, thereby generating masked data in which at least a portion of an area of ​​the target item present in the image data is masked; An operation display unit that displays information and accepts operations, the operation display unit accepts an item designation operation for designating any one of the items; the control unit recognizes the item specified by the item specifying operation as the target item, and performs, as the masking process, a process of masking at least an area of ​​the item value among areas of the target item present in the image data to generate masked data; After generating the masked data, the control unit causes the operation display unit to display a preview image showing contents of the masked data, and causes the operation display unit to accept an editing operation for editing the masked data; The editing operation is An operation of newly adding any one of the items as a masking target; and removing the masked item from the masking target, When the editing operation is performed, the control unit newly generates the masked data that reflects the editing content of the performed editing operation.

4. an output unit that performs output processing of the masked data, The image processing device according to any one of claims 1 to 3, wherein the output unit is at least one of a printing unit that performs printing an image based on the masked data onto a sheet as the output process, and a communication unit that performs transmitting the masked data to an external device as the output process.

Images