Image processing system, image processing method, and program

Abstract

To reduce the user's workload in resolving image defects in image data obtained through the scanning function. [Solution] The image processing system 1 includes an image acquisition unit 23 that acquires image data of a scanned document based on specified scan settings, an image determination unit 25 that analyzes the image data acquired by the image acquisition unit 23 and determines whether or not there are image defects, an attribute determination unit 26 that determines the attributes of the image defects if the image determination unit 25 determines that there are image defects, and a presentation unit 27 that presents the determination result of the attribute determination unit 26 to the user.

Description

[[Technical Field]]

[0001] The present invention relates to an image processing system, an image processing method, and a program. [[Background Art]]

[0002] An image processing apparatus such as an MFP (Multifunction Peripheral) has a scanning function, and can read an image of a document to generate image data. Image data read by the scanning function may include image defects such as show-through and streaks.

[0003] Conventionally, in order to improve the detection accuracy of streaks contained in image data generated by a scanning function, it has been proposed to generate learning data from image data containing streaks in an image processing apparatus (for example, Patent Document 1). In this conventional technology, based on the learning data generated by the image processing apparatus, a server device performs learning for detecting streaks and generates a learning model. When the image processing apparatus newly generates image data through the scanning function, it inputs the image data into the learning model generated by the server device, thereby detecting streaks of various patterns.

[0004] However, among image defects contained in image data generated by a scanning function, there are various other defects besides streaks. For example, show-through that may occur when scanning a double-sided document is included in image defects. In addition, image defects such as image crushing, density defects, and blurring may also occur. The above conventional technology detects streaks as image defects when streaks are contained in image data. However, the conventional technology cannot appropriately detect image defects other than streaks.

[0005] By the way, when an image obtained by a scanning function contains an image defect, there are various causes thereof. For example, among various image defects, there are also defects that can be eliminated by changing scanning settings.

[0006] However, even if the scanned image contains image defects, users may not know how to resolve them. In particular, even if the defect can be resolved by changing the scan settings, users may not know which settings to change. Therefore, users have to repeatedly experiment with the many settings included in the scan settings, which results in a time-consuming process to resolve the defect. [Prior art documents] [Patent Documents]

[0007] [Patent Document 1] Japanese Patent Publication No. 2021-150856 [Overview of the project] [Problems that the invention aims to solve]

[0008] This invention was made to solve the above-mentioned conventional problems. Specifically, the present invention aims to provide an image processing system, an image processing method, and a program that can reduce the workload of the user in resolving image defects when image data obtained by a scanning function contains such defects. [Means for solving the problem]

[0009] To achieve the above objective, the invention according to claim 1 is an image processing system comprising: an image acquisition unit that acquires image data obtained by scanning a document based on specified scan settings; an image determination unit that analyzes the image data acquired by the image acquisition unit and determines whether or not an image defect exists; an attribute determination unit that determines the attribute of the image defect if the image determination unit determines that an image defect exists; and a presentation unit that presents the determination result of the attribute determination unit to the user.

[0010] The invention according to claim 2 is characterized in that, in the image processing system of claim 2, the attribute determination unit determines whether the attribute of the image defect is an image defect that can be resolved by changing the scan settings or an image defect that cannot be resolved by changing the scan settings.

[0011] The invention according to claim 3 is characterized in that, in the image processing system of claim 2, when the attribute determination unit determines that an image defect may be resolved by changing the scan settings, it identifies the setting item that should be changed.

[0012] The invention according to claim 4 is an image processing system according to claim 3, characterized in that the display unit displays the setting items identified by the attribute determination unit on a predetermined display unit and prompts the user to change the scan settings.

[0013] The invention according to claim 5 is an image processing system according to claim 4, characterized in that the image acquisition unit reacquires image data scanned from a document based on scan settings changed by the user.

[0014] The invention according to claim 6 is an image processing system according to claim 3, further comprising a setting change unit that automatically changes the scan settings by rewriting the setting values ​​of setting items identified by the attribute determination unit, wherein the image acquisition unit is configured to acquire image data by scanning a document placed on a flatbed type document placement surface, and the setting change unit, after automatically changing the scan settings, causes the image acquisition unit to reacquire the image data.

[0015] The invention according to claim 7 is characterized in that, in the image processing system of claim 6, the setting change unit repeatedly performs a process to automatically change the scan settings and cause the image acquisition unit to reacquire image data until the image determination unit determines that there are no image defects.

[0016] The invention according to claim 8 is an image processing system according to any one of claims 1 to 7, wherein the image determination unit determines whether or not an image defect exists in the image data by inputting image data acquired by the image acquisition unit to a learning model that has learned images with image defects and images without image defects.

[0017] The invention according to claim 9 is an image processing system according to claim 8, further comprising a learning model generation unit for generating the learning model, wherein, after the scan settings have been changed, if it is determined that there are no image defects in the image data reacquired by the image acquisition unit, the learning model generation unit learns the image data with image defects before the scan settings were changed and the image data without image defects after the scan settings were changed, and updates the learning model.

[0018] The invention according to claim 10 is an image processing method characterized by comprising: an image acquisition step of acquiring image data obtained by scanning a document based on specified scan settings; an image determination step of analyzing the image data acquired by the image acquisition step and determining whether or not an image defect exists; an attribute determination step of determining the attributes of the image defect if the image determination step determines that an image defect exists; and a presentation step of presenting the determination result from the attribute determination step to the user.

[0019] The invention according to claim 11 is a program executed in an image processing apparatus that generates image data by scanning a document, wherein the program causes the image processing apparatus to execute: an image acquisition step of acquiring image data obtained by scanning a document based on a specified scan setting; an image determination step of analyzing the image data acquired in the image acquisition step and determining whether an image defect exists; an attribute determination step of determining an attribute of the image defect when it is determined in the image determination step that an image defect exists; and a presentation step of presenting a determination result obtained by the attribute determination step to a user. [Effects of the Invention]

[0020] According to the present invention, when image data obtained by a scanning function includes an image defect, it is possible to reduce the user's work burden for eliminating the image defect. [Brief Description of the Drawings]

[0021] [Figure 1] It is a diagram showing a schematic configuration of an image processing system. [Figure 2] It is a block diagram showing a functional configuration of an image processing system. [Figure 3] It is a diagram showing an example of learning by a learning model generation unit. [Figure 4] It is a diagram showing a concept of processing by a defect determination unit. [Figure 5] It is a diagram showing an example of attribute information. [Figure 6] It is a diagram showing an example of a preview screen. [Figure 7] It is a diagram showing an example of a preview screen. [Figure 8] It is a diagram showing an example of a preview screen. [Figure 9] It is a flowchart showing an example of a processing procedure in an image processing system. [Figure 10] It is a diagram showing an example of attribute information. [Figure 11]This is a flowchart illustrating an example of a processing procedure in an image processing system. [Figure 12] This is a flowchart illustrating an example of a processing procedure in an image processing system. [Figure 13] This flowchart shows an example of a detailed procedure for the rescan process. [Modes for carrying out the invention]

[0022] Preferred embodiments of the present invention will be described in detail below with reference to the drawings. In the embodiments described below, elements common to all are denoted by the same reference numerals, and redundant explanations of these elements will be omitted.

[0023] (First Embodiment) Figure 1 shows an example configuration of an image processing system 1 in a first embodiment of the present invention. As shown in Figure 1, the image processing system 1 has a configuration in which an image processing device 2 and a server device 4 are connected to each other via a network 3. Figure 1 illustrates a case in which the image processing device 2 and the server device 4 are configured as separate devices. However, it is not limited to this. For example, the functions of the server device 4, which will be described later, may be incorporated into the image processing device 2.

[0024] The image processing device 2 is comprised of, for example, an MFP (Multifunction Printer). Therefore, the image processing device 2 has multiple functions such as scanning, copying, and printing. The image processing device 2 has a scanner unit 5 at the top of the main body. The scanner unit 5 optically reads the image of a document set by the user and generates image data. The image processing device 2 also has a printer unit 6 in the center of the main body. The printer unit 6 prints and outputs an image on a sheet such as printing paper based on the image data designated as the print target. The image processing device 2 also has an operation panel 7 on the front side of the main body. The operation panel 7 is the user interface when the user uses the image processing device 2. As shown in Figure 2, the operation panel 7 comprises a display unit 7a and an operation unit 7b. The display unit 7a is comprised of, for example, a color liquid crystal display and displays an operation screen that the user can operate. The operation unit 7b is comprised of, for example, a touch screen and accepts user input on the operation screen.

[0025] The scanner unit 5 includes an image reading unit 8 and an automatic document transport unit 9. The image reading unit 8 can read images using either a sheet feeder type image reading method or a flatbed type image reading method.

[0026] For example, when a document is placed in the document tray 9a of the automatic document transport unit 9, the scanner unit 5 causes the image reading unit 8 to perform sheet-feed type image reading. That is, the scanner unit 5 drives the automatic document transport unit 9 to automatically transport the documents one by one to the image reading position of the image reading unit 8. The image reading unit 8 reads the image of the document as it passes through the image reading position and generates image data.

[0027] The automatic document transport unit 9 has a flatbed-type document placement surface located on the upper surface of the image reading unit 8 that can be opened and closed. The user can place a document on the document placement surface by lifting the automatic document transport unit 9. When a document is placed on the flatbed-type document placement surface, the scanner unit 5 causes the image reading unit 8 to perform flatbed-type image reading. That is, the image reading unit 8 drives a reading head located opposite the document across the platen glass to read the image of the document in the main scanning direction and the sub-scanning direction, and generates image data.

[0028] When the image processing device 2 acquires image data through its scanning function, it analyzes the image data and determines whether or not there are image defects. If image defects are found, the image processing device 2 presents the user with a method to resolve the image defects. In this processing process, the image processing device 2 is configured to cooperate with the server device 4.

[0029] Figure 2 is a block diagram showing the functional configuration of the image processing system 1. The image processing device 2 includes a scanner unit 5 and an operation panel 7, as well as a control unit 10, a storage unit 11, and a communication interface 12.

[0030] The control unit 10 comprehensively controls the operation of the image processing device 2. The control unit 10 includes a hardware processor (not shown) and memory. The hardware processor reads and executes the program 13 stored in the storage unit 11. At this time, the hardware processor uses the memory storage area as a work area.

[0031] The storage unit 11 is a non-volatile storage device consisting of a hard disk drive (HDD) or a solid-state drive (SSD). The storage unit 11 pre-stores a program 13 that is executed by the hardware processor of the control unit 10. The storage unit 11 also stores attribute information 14. Details of the attribute information 14 will be described later.

[0032] The communication interface 12 connects the image processing device 2 to the network 3 and communicates with external devices connected to the network 3. The control unit 10 communicates with the server device 4 via this communication interface 12 and operates in cooperation with the server device 4.

[0033] The control unit 10 functions as a setting unit 21, an image acquisition unit 23, a determination unit 24, a presentation unit 27, and an image output unit 28 when the hardware processor executes the program 13.

[0034] The setting unit 21 configures the job. For example, if the user selects the scan function, the setting unit 21 displays a settings screen for the scan job on the display unit 7a. The setting unit 21 detects user operations on the settings screen via the operation unit 7b and configures the scan settings for document scanning. For example, the setting unit 21 sets the user-specified value for the setting items among the multiple setting items included in the scan settings that the user has specified. The setting unit 21 also sets the setting values ​​of the setting items among the multiple setting items included in the scan settings that were not specified by the user to predetermined default values.

[0035] The setting unit 21 includes a setting change unit 22. The setting change unit 22 changes the scan settings set by the setting unit 21. For example, the setting change unit 22 changes at least some of the scan settings applied during document scanning. The setting change unit 22 changes the scan settings applied during the previous document scanning based on a setting change operation by the user. The setting change unit 22 can also automatically change at least some of the scan settings applied during the previous document scanning.

[0036] The image acquisition unit 23 drives the scanner unit 5 to perform a document reading operation (scanning operation) based on the user's scan instruction. The image acquisition unit 23 then acquires image data generated by the image reading unit 8 as a result of the document reading operation. In other words, the image acquisition unit 23 acquires image data by scanning the document using the scan settings set by the setting unit 21. Once the image acquisition unit 23 acquires image data from the image reading unit 8, it provides that image data to the determination unit 24.

[0037] The determination unit 24 performs various determinations based on the image data acquired by the image acquisition unit 23. The determination unit 24 includes an image determination unit 25 and an attribute determination unit 26.

[0038] The image determination unit 25 analyzes the image data acquired by the image acquisition unit 23 and determines whether or not there are image defects in the image data. For example, the image determination unit 25 works in cooperation with the server device 4 to determine whether or not there are image defects in the image data and obtains the determination result.

[0039] The configuration of the server device 4 will now be explained. As described above, the server device 4 works in cooperation with the image determination unit 25 to determine whether or not there are image defects in the image data. Therefore, the server device 4 functions as part of the image determination unit 25.

[0040] The server device 4 comprises a control unit 30, a storage unit 31, and a communication interface 32. The control unit 30 comprises a hardware processor (not shown) and memory. By reading and executing a predetermined program, the hardware processor enables the control unit 30 to function as a learning model generation unit 33 and a malfunction detection unit 35. The storage unit 31 is a non-volatile storage device composed of a hard disk drive (HDD) or a solid-state drive (SSD). The learning model 34 generated by the learning model generation unit 33 is stored in the storage unit 31. The communication interface 32 connects the server device 4 to the network 3 and communicates with external devices connected to the network 3. The control unit 30 communicates with the image processing device 2 via this communication interface 32 and operates in cooperation with the image processing device 2.

[0041] The learning model generation unit 33 learns from training data that includes image data without image defects and image data with image defects, and generates a learning model 34 for detecting image defects contained in the image data.

[0042] Figure 3 shows an example of learning by the learning model generation unit 33. For example, the learning model generation unit 33 receives image data D1, which does not contain image defects, and image data D2, which contains image defects, as learning data TD. For example, image data D1 and D2 are data generated from the same image. Image data D1 does not contain image defects and therefore shows a normal image. In contrast, image data D2 has an image defect called "image distortion," and its image quality is lower than that of image data D1. Note that image data D1 and D2 may be data actually generated by the image reading unit 8, or they may be data intentionally generated by a device other than the image reading unit 8. Image defects in image data D2 include not only image distortion, but also density defects, back-image, blurring, streaks, and scan defects.

[0043] The learning model generation unit 33 compares image data D1 and D2 included in the training data TD shown in Figure 3 and learns the types of image defects contained in image data D2. Then, the learning model generation unit 33 generates a learning model 34 based on the learning results. By learning various types of training data TD in advance, the learning model generation unit 33 generates a learning model 34 that can detect various patterns of image defects contained in the image data generated by the image reading unit 8. Furthermore, if the learning model generation unit 33 acquires new training data TD and learns from it, it can also update the existing learning model 34 based on the learning results.

[0044] The defect detection unit 35 functions when it acquires image data that is subject to image defect detection. When the defect detection unit 35 acquires image data that is subject to image defect detection, it determines whether or not an image defect exists in that image data. Figure 4 is a diagram illustrating the concept of processing by the defect detection unit 35. When the defect detection unit 35 acquires image data D3 that is subject to image defect detection, it reads the learning model 34 from the storage unit 31. The defect detection unit 35 then inputs the image data D3 to the learning model 34 to determine whether or not an image defect exists in the image data D3 and outputs the determination result.

[0045] The defect detection unit 35 identifies the nature of the image defect if one exists in the image data D3. Image defects include image distortion, poor density, bleed-through, blurring, streaks, and scanning errors. For example, as shown in Figure 4, if the image data D3 contains an image distortion defect, the defect detection unit 35 identifies that the image data D3 contains an image distortion defect. The identified image defect is included in the determination result output from the defect detection unit 35.

[0046] Returning to Figure 2, the image determination unit 25 transmits the image data acquired by the image acquisition unit 23 to the server device 4 via the communication interface 12 and requests a defect determination from the defect determination unit 35. As a result, the defect determination unit 35 inputs the image data acquired by the image acquisition unit 23 into the learning model 34 and determines whether or not an image defect exists in the image data. The defect determination unit 35 transmits the determination result to the image determination unit 25 via the communication interface 32. In this manner, the image determination unit 25 in this embodiment works in cooperation with the server device 4 to determine whether or not an image defect exists in the image data acquired by the image acquisition unit 23 and obtains the determination result.

[0047] Furthermore, the image determination unit 25 may incorporate the functions of the server device 4 described above. In that case, the image determination unit 25 can determine whether or not there are image defects in the image data and obtain the determination result without communicating via the network 3.

[0048] The image determination unit 25 terminates the processing by the determination unit 24 if there are no image defects in the image data acquired by the image acquisition unit 23. In this case, the image processing device 2 performs the normal processing after image data acquisition in the scan job. For example, the image processing device 2 displays a preview image based on the image data acquired by the image acquisition unit 23 on the display unit 7a and outputs the image data to the output destination specified by the user.

[0049] In response to this, the image determination unit 25 activates the attribute determination unit 26 in the determination unit 24 if there is an image defect in the image data acquired by the image acquisition unit 23.

[0050] If the image determination unit 25 determines that an image defect exists, the attribute determination unit 26 determines the attribute of that image defect. Image defects have two attributes: defects that can be resolved by changing the scan settings and defects that cannot be resolved by changing the scan settings. The attribute determination unit 26 determines which of these two attributes the image defect present in the image data has. Furthermore, if the attribute determination unit 26 determines that the image defect is one that can be resolved by changing the scan settings, it identifies the setting item that should be changed in order to resolve the defect.

[0051] When determining the attribute of an image defect, the attribute determination unit 26 reads attribute information 14 from the storage unit 11. Based on the attribute information 14 and the determination result from the image determination unit 25, the attribute determination unit 26 determines the attribute of the image defect.

[0052] Figure 5 shows an example of attribute information 14. As shown in Figure 5, attribute information 14 is information that correlates the content of an image defect, the possibility of resolving the defect by changing settings, and the setting items. The "Content of Image Defect" column contains all of the content of multiple image defects identified by the defect determination unit 35. The "Possibility of Resolving the Defect by Changing Settings" column defines whether the defect can be resolved by changing the scan settings for each of the image defects identified by the defect determination unit 35. For example, if the defect can be resolved by changing settings, "YES" is written in the "Possibility of Resolving the Defect" column. Conversely, if the defect cannot be resolved by changing settings, "NO" is written in the "Possibility of Resolving the Defect" column. The "Setting Items" column describes the setting items needed to resolve the defect if it can be resolved by changing settings. Therefore, the attribute determination unit 26 can determine whether the image defect identified by the image determination unit 25 can be resolved by changing the scan settings by referring to the attribute information 14 as shown in Figure 5. Furthermore, if the problem can be resolved by changing the scan settings, the attribute determination unit 26 can identify the setting item that should be changed by referring to the attribute information 14.

[0053] When the determination unit 24 has finished the determination processing on the image data acquired by the image acquisition unit 23, it activates the presentation unit 27.

[0054] The presentation unit 27 presents the determination result made by the determination unit 24 to the user. For example, the presentation unit 27 presents the information to the user by displaying it on the display unit 7a. However, the presentation method by the presentation unit 27 is not limited to this. For example, the presentation unit 27 may provide the information to the user by voice, or it may provide the information by transmitting it to an external device such as a mobile terminal.

[0055] For example, once the determination process by the determination unit 24 is completed, the presentation unit 27 generates a preview screen for displaying a preview image based on the image data. The presentation unit 27 then displays this preview screen on the display unit 7a. For example, if the attribute determination unit 26 identifies setting items that need to be changed to resolve image defects, the presentation unit 27 generates a preview screen prompting the user to change the scan settings and displays this preview screen on the display unit 7a.

[0056] Figures 6 to 8 show examples of preview screens G1 displayed by the display unit 27. Figure 6 shows the preview screen G1 when it is determined that there are no image defects in the image data acquired by the scan job. The preview screen G1 has a preview area 40 in the center of the screen that displays a preview image based on the image data acquired by the scan job. The preview area 40 displays a normal preview image 41 in which there are no image defects. In addition, a group of operation buttons 42 that can be operated by the user is displayed to the right of the preview area 40. By operating the operation buttons 42, the user can enlarge or reduce the preview image 41 and check the details of the preview image 41. The user can also stop or continue processing the image data by operating the operation buttons 42.

[0057] Figure 7 shows a preview screen G1 when an image defect can be resolved by changing the scan settings. If an image defect exists in the image data and that defect can be resolved by changing the scan settings, the display unit 27 displays a preview screen G1 as shown in Figure 7(a). That is, the display unit 27 displays a preview image 43 in the preview area 40 based on the image data in which the image defect exists. For example, the preview image 43 includes an image defect where the image is distorted.

[0058] Furthermore, the display unit 27 displays a message display area 44 in a display area adjacent to the preview area 40. The message display area 44 displays a message indicating that there is an image defect in the image data acquired by the scan job, and that the image defect may be resolved by changing the scan settings. The message display area 44 also displays user-operable setting change buttons 45.

[0059] When the user operates the setting change button 45, the display unit 27 transitions from the preview screen G1 shown in Figure 7(a) to the preview screen G1 shown in Figure 7(b). In other words, the display unit 27 switches the message display area 44 to the button display area 46. The button display area 46 shown in Figure 7(b) displays multiple operation buttons for changing the setting values ​​of the setting items that resolve image defects. The user can change the scan settings by operating one of these operation buttons.

[0060] For example, the preview screen G1 in Figure 7(b) displays a button area 46 for changing the resolution setting, and shows that the current resolution is "200 x 200 dpi". By operating on this button area 46, the user can change the resolution used for scanning documents to a higher resolution than the current resolution.

[0061] When a user attempts to change the scan settings, the setting change unit 22 in the setting unit 21 becomes functional. The setting change unit 22 then changes the scan settings based on the user's setting change operation.

[0062] Furthermore, the button display area 46 displays the rescan button 47. When the rescan button 47 is operated after the user has changed the scan settings, the image acquisition unit 23 functions. Based on the user's rescan instruction, the image acquisition unit 23 drives the scanner unit 5 to perform the document reading operation. At this time, the document reading operation is performed with the settings changed by the setting change unit 22 applied. Therefore, the image acquisition unit 23 can acquire image data in which image defects have been resolved.

[0063] Incidentally, image defects in image data may not be resolved by changing the scan settings. In that case, the display unit 27 displays the preview screen G1 shown in Figure 8. The preview screen G1 in Figure 8 displays a preview image 51 containing streaks 52 in the preview area 40. If the image defect is streaks 52, the defect will not be resolved simply by changing the scan settings. For example, if streaks 52 appear in the image data, it is often because dust is attached to the glass surface of the image reading unit 8. Therefore, if the image defect in the image data is streaks, the display unit 27 displays a message display area 53 prompting the user to clean or perform maintenance. This allows the user to remove the cause of the image defect by performing cleaning or maintenance.

[0064] The message display area 53 also displays the rescan button 54. When the rescan button 54 is pressed after cleaning or maintenance by the user, the image acquisition unit 23 functions. Based on the user's rescan instruction, the image acquisition unit 23 drives the scanner unit 5 to perform the document scanning operation.

[0065] Returning to Figure 2, the image output unit 28 functions when the user issues an instruction to output image data. The image output unit 28 then outputs the image data to the output destination specified by the user. For example, if it is specified that the image data obtained by a scan job be sent to an external device, the image output unit 28 sends the image data to the external device via the communication interface 12. Also, if print output is specified, the image output unit 28 outputs the image data to the printer unit 6.

[0066] Next, the processing procedure in the image processing system 1 will be described. Figure 9 is a flowchart showing an example of the processing procedure in the image processing system 1. This processing is mainly performed by the control unit 10 of the image processing device 2.

[0067] The image processing device 2 accepts the user's operation to set up a scan job (step S10). The image processing device 2 then sets up the scan for document reading by reflecting the settings specified by the user (step S11). Subsequently, the image processing device 2 executes the scan job based on the user's scan instruction (step S12). That is, the image processing device 2 reads the image of the document set by the user. As a result, the image processing device 2 acquires image data of the document scanned based on the specified scan settings (step S13).

[0068] When the image processing device 2 acquires image data from a scanned document, it analyzes the image data and performs an image determination to determine whether or not there are any image defects (step S14). At this time, the image processing device 2 works in cooperation with the server device 4 to perform image analysis using the learning model 34 and determines whether or not there are any image defects in the image data. If, as a result, there are image defects in the image data (YES in step S15), the image processing device 2 determines the attributes of the image defects (step S16). Then, based on the result of the attribute determination, the image processing device 2 determines whether or not the image defects in the image data can be resolved by changing the scan settings (step S17).

[0069] If the image defect can be resolved by changing the scan settings (YES in step S17), the image processing device 2 identifies the setting item that should be changed to resolve the image defect from among the multiple setting items included in the scan settings (step S18). The image processing device 2 then displays the preview screen G1 on the display unit 7a (step S19). For example, the image processing device 2 displays the preview screen G1 shown in Figures 7(a) and 7(b) on the display unit 7a. Therefore, the user can easily understand the setting item needed to resolve the image defect and immediately perform the appropriate setting change operation. Subsequently, the image processing device 2 accepts the user's setting change operation to change the scan settings (step S20).

[0070] When the user performs a setting change operation, the image processing device 2 returns to step S11. That is, the image processing device 2 changes the scan settings applied during the previous scan based on the user's setting change operation and executes the processes from step S11 onwards again in order to perform a rescan.

[0071] On the other hand, if the image defect is not resolved by changing the scan settings (NO in step S17), the image processing device 2 will guide the user to perform maintenance (step S21). At this time, the image processing device 2 will display a preview screen G1 on the display unit 7a, for example, as shown in Figure 8. Therefore, the user can promptly perform appropriate maintenance to resolve the image defect.

[0072] Furthermore, if the image processing device 2 determines, after performing image judgment (step S14), that there are no image defects in the image data (NO in step S15), it displays the preview screen G1 on the display unit 7a (step S22). At this time, the image processing device 2 displays the preview screen G1 on the display unit 7a as shown in Figure 6. Then, upon detecting an image output instruction from the user, the image processing device 2 outputs the image data acquired by scanning the document to the output destination specified by the user (step S23). This completes the processing by the image processing device 2.

[0073] As described above, the image processing system 1 of this embodiment acquires image data by scanning a document based on the specified scan settings. The image processing system 1 analyzes the acquired image data and determines whether or not there are image defects. If it determines that there are image defects, the image processing system 1 further determines the attributes of the image defects. This attribute determination reveals whether or not the image defects can be resolved by changing the scan settings. The image processing system 1 then presents these determination results to the user. The image processing system 1 configured in this way automatically determines and presents to the user whether or not image defects can be resolved by changing the scan settings when image data obtained by scanning a document contains image defects, thereby reducing the burden on the user.

[0074] (Second Embodiment) Next, a second embodiment of the present invention will be described. In the first embodiment, an example of processing when an image defect exists in the image data obtained by scanning a document, and the image defect is resolved by changing the scan settings was described. That is, the image processing system 1 of the first embodiment presents the user with the setting items to be changed and accepts the user's operation to change the settings. In contrast, in this embodiment, an example of automatically changing the scan settings in the image processing system 1 will be described. The configuration example of the image processing system 1 in this embodiment is the same as that described in the first embodiment.

[0075] The image processing system 1 of this embodiment stores attribute information 14 in the storage unit 11 that is different from that of the first embodiment. Figure 10 shows an example of the attribute information 14. As shown in Figure 10, the attribute information 14 is information that correlates the content of the image defect, the possibility of resolving the defect by changing settings, the setting items, and the setting changes. The content of the image defect, the possibility of resolving the defect by changing settings, and the setting items are the same as the attribute information 14 shown in Figure 5. The setting change column defines the content of the setting changes for the setting items shown in the setting item column. Therefore, the image processing device 2 can identify the setting items that should be changed to resolve the image defect and the content of the setting changes for those setting items by referring to the attribute information 14.

[0076] Figure 11 is a flowchart illustrating an example of a processing procedure in the image processing system 1. This processing is primarily performed by the control unit 10 of the image processing device 2. This processing is applicable when a document is placed on the flatbed type document placement surface of the image processing device 2.

[0077] The image processing device 2 accepts the user's setting operation for a scan job (step S30). The image processing device 2 then sets up the scan for document reading by reflecting the setting values ​​specified by the user (step S31). Subsequently, the image processing device 2 executes the scan job based on the user's scan instruction (step S32). The image processing device 2 then acquires image data of the document scanned based on the specified scan settings (step S33).

[0078] When the image processing device 2 acquires image data from a scanned document, it analyzes the image data and performs an image determination to determine whether or not there are image defects (step S34). At this time, the image processing device 2 works in cooperation with the server device 4 to perform image analysis using the learning model 34 and determines whether or not there are image defects in the image data. If an image defect is found in the image data (YES in step S35), the image processing device 2 determines the attribute of the image defect by referring to the attribute information 14 shown in Figure 10 (step S36). Then, based on the result of the attribute determination, the image processing device 2 determines whether or not the image defect present in the image data can be resolved by changing the scan settings (step S37).

[0079] If the image defect can be resolved by changing the scan settings (YES in step S37), the image processing device 2 identifies the setting item that should be changed to resolve the image defect from among the multiple setting items included in the scan settings (step S38). The image processing device 2 reads the setting value of the identified setting item and determines whether the setting value has already reached the limit value (step S39). If the setting value has not reached the limit value (NO in step S39), the image processing device 2 automatically changes the setting value of the identified setting item based on the attribute information 14 (step S40). As a result, the scan settings for the next time a document is scanned are automatically changed. After that, the processing by the image processing device 2 returns to step S32. In other words, the image processing device 2 applies the automatically changed scan settings and automatically executes the scan job again. As described above, if the document is placed on the document placement surface of a flatbed type scanner, there is no need to prompt the user to re-set the document. Therefore, the image processing device 2 can quickly and automatically execute a rescan after automatically changing the scan settings.

[0080] On the other hand, if the image defect is not resolved by changing the scan settings (NO in step S37), the image processing device 2 displays to the user that an image defect exists in the image data (step S41). Also, if the setting value has already reached the limit value (YES in step S39), the image processing device 2 displays to the user that an image defect exists in the image data, similar to the above (step S41).

[0081] Furthermore, if the image processing device 2 determines, after performing image judgment (step S34), that there are no image defects in the image data (NO in step S35), it displays the preview screen G1 on the display unit 7a (step S42). Then, upon detecting an image output instruction from the user, the image processing device 2 outputs the image data obtained by scanning the original document to the output destination specified by the user (step S43). This completes the processing by the image processing device 2.

[0082] In the processing procedure described above, the image processing device 2 may repeatedly execute the processes in steps S32 to S40. For example, if an image defect can be resolved by increasing the resolution during document scanning, the image processing device 2 will change the resolution setting to a higher level one step at a time. Each time the scan setting is changed, the image processing device 2 will execute a scan job and determine whether or not there are image defects in the image data acquired by that scan job. Therefore, the image processing device 2 can resolve image defects by repeatedly executing the processes in steps S32 to S40. In this case, since the image processing device 2 does not require user intervention, it can automatically repeat the process of changing the scan settings and reacquiring image data. Thus, the image processing device 2 can acquire image data free of image defects while reducing the burden on the user.

[0083] (Third embodiment) Next, a third embodiment of the present invention will be described. For example, the learning model 34 used to determine whether or not image defects exist in image data is preferably updated sequentially. Therefore, the third embodiment describes an example in which image data with image defects and image data without image defects are used as training data to update the learning model 34. The configuration example of the image processing system 1 in this embodiment is the same as that described in the first embodiment.

[0084] Figures 12 and 13 are flowcharts illustrating an example of a processing procedure in the image processing system 1. This processing is primarily performed by the control unit 10 of the image processing device 2. Furthermore, this processing is applicable when a document is placed on a flatbed-type document placement surface in the image processing device 2, similar to the second embodiment.

[0085] The image processing device 2 accepts the user's setting operation for a scan job (step S50). The image processing device 2 then sets up the scan for document reading by reflecting the setting values ​​specified by the user (step S51). Subsequently, the image processing device 2 executes the scan job based on the user's scan instruction (step S52). The image processing device 2 then acquires image data of the document scanned based on the specified scan settings (step S53).

[0086] When the image processing device 2 acquires image data from a scanned document, it analyzes the image data and performs an image determination to determine whether or not there are any image defects (step S54). At this time, the image processing device 2 works in cooperation with the server device 4 to perform image analysis using the learning model 34 and determines whether or not there are any image defects in the image data. If, as a result, there are image defects in the image data (YES in step S55), the image processing device 2 performs a rescan process (step S56).

[0087] If there are no image defects in the image data (NO in step S55), the image processing device 2 displays the preview screen G1 on the display unit 7a (step S57). Then, upon detecting the user's instruction to output the image, the image processing device 2 outputs the image data obtained by scanning the original document to the output destination specified by the user (step S58).

[0088] Figure 13 is a flowchart showing an example of a detailed processing procedure for the rescan process (step S56). At the start of the rescan process, the image processing device 2 temporarily stores the image data acquired in the previous scan in the storage unit 11 or the like (step S60). After saving the image data, the image processing device 2 determines the attribute of the image defect by referring to the attribute information 14 shown in Figure 10 (step S61). Based on the result of the attribute determination, the image processing device 2 determines whether the image defect present in the image data can be resolved by changing the scan settings (step S62). If the image defect cannot be resolved by changing the scan settings (NO in step S62), the image processing device 2 displays to the user that an image defect exists in the image data (step S74).

[0089] If the image defect can be resolved by changing the scan settings (YES in step S62), the image processing device 2 identifies the setting item that should be changed to resolve the image defect from among the multiple setting items included in the scan settings (step S63). The image processing device 2 reads the setting value of the identified setting item and determines whether the setting value has already reached the limit value (step S64). If the setting value has already reached the limit value (YES in step S64), the image processing device 2 cannot make the setting change to resolve the image defect. Therefore, in this case, the image processing device 2 displays to the user that there is an image defect in the image data (step S74).

[0090] On the other hand, if the setting value has not reached the limit (NO in step S64), the image processing device 2 automatically changes the setting value of the identified setting item based on the attribute information 14 (step S65). This automatically changes the scan settings for the next time the document is scanned.

[0091] The image processing device 2 changes the scan settings and then performs a rescan (step S66). The image processing device 2 acquires the image data generated by the rescan (step S67).

[0092] When the image processing device 2 acquires image data through a rescan, it analyzes the image data and performs an image determination to determine whether or not there are image defects (step S68). At this time, the image processing device 2 also works in cooperation with the server device 4 to perform image analysis using the learning model 34 and determines whether or not there are image defects in the image data. If an image defect is found in the image data (YES in step S69), the processing by the image processing device 2 returns to step S60. The image processing device 2 then repeats the processing from step S60 onward. This resolves the image defects.

[0093] When the image data obtained through rescanning is free of image defects (NO in step S69), the image processing device 2 saves the image data free of image defects to the storage unit 11 or the like (step S70). Subsequently, the image processing device 2 reads the image data saved in step S60 and the image data saved in step S70, and generates training data including these image data (step S71). The image processing device 2 then sends the training data to the server device 4, causing the server device 4 to perform training processing based on the training data (step S72). At this time, the server device 4 activates the training model generation unit 33 and executes training processing to detect image defects based on the training data obtained from the image processing device 2.

[0094] The image processing device 2 causes the server device 4 to perform the learning process, and then causes the server device 4 to update the learning model 34 (step S73). Therefore, the learning model generation unit 33 of the server device 4 updates the learning model 34 using the learning results based on the training data. With this, the rescan process is completed.

[0095] In other words, in this embodiment, if an image defect is resolved by changing the scan settings, the image processing system 1 updates the learning model 34 using training data that combines image data containing the image defect and image data without the image defect. Therefore, the image processing system 1 can sequentially update the learning model 34 for detecting image defects. As the learning model 34 is sequentially updated, the image processing system 1 can gradually improve the accuracy of its determination of whether or not an image defect exists.

[0096] (modified version) Preferred embodiments of the present invention have been described above. However, the present invention is not limited to those described in the above embodiments, and various modifications are applicable.

[0097] For example, if the functions of the server device 4 described above are incorporated into the image processing device 2, the image processing system 1 consists only of the image processing device 2. The image processing system 1 may have such a configuration.

[0098] Furthermore, in the above embodiment, an example was described in which the learning model 34 is used to determine whether or not there are image defects in the image data acquired by the scanning function. However, it is not necessary to use the learning model 34 when determining whether or not there are image defects. For example, the image processing system 1 may determine whether or not there are image defects in the image data by performing a generally known image analysis.

[0099] Furthermore, in the above embodiment, an example was described in which the program 13 executed in the image processing device 2 is pre-stored in the storage unit 11. However, the program 13 can be traded on its own. Therefore, the program 13 may be a program that is installed in the image processing device 2 as needed. In this case, the program 13 is provided in a manner that allows it to be downloaded, for example, via a network such as the Internet. Alternatively, the program 13 may be provided in a manner that is recorded on a recording medium such as a CD-ROM or USB memory. The program 13 may also be provided in other manners. [Explanation of symbols]

[0100] 1. Image Processing System 2 Image Processing Device 3 Network 4 Server Devices 5. Scanner section 7. Control Panel 7a Display section 8 Image reading unit 10 Control Unit 11 Storage section 21 Settings Section 22. Settings Change Section 23 Image acquisition unit 24 Judgment section 25 Image determination unit 26 Attribute determination section 27 Presentation section 28 Image output section 30 Control Unit 33. Learning Model Generation Unit 34 Learning Models 35. Defect detection unit

Claims

1. An image acquisition unit that acquires image data of a scanned document based on specified scan settings, An image determination unit analyzes image data acquired by the image acquisition unit and determines whether or not an image defect exists, If the image determination unit determines that an image defect exists, an attribute determination unit determines the attributes of the image defect, A presentation unit that presents the determination result from the attribute determination unit to the user, An image processing system characterized by comprising the following features.

2. The image processing system according to claim 1, characterized in that the attribute determination unit determines whether the attribute of the image defect is an image defect that may be resolved by changing the scan settings or an image defect that cannot be resolved by changing the scan settings.

3. The image processing system according to claim 2, characterized in that when the attribute determination unit determines that an image defect may be resolved by changing the scan settings, it identifies the setting item that should be changed.

4. The image processing system according to claim 3, characterized in that the display unit displays the setting items identified by the attribute determination unit on a predetermined display unit and prompts the user to change the scan settings.

5. The image processing system according to claim 4, characterized in that the image acquisition unit reacquires image data of a scanned document based on scan settings changed by the user.

6. A setting change unit that automatically changes the scan settings by rewriting the setting values ​​of the setting items identified by the attribute determination unit. Furthermore, The image acquisition unit is configured to acquire image data by scanning a document placed on a flatbed type document placement surface. The image processing system according to claim 3, characterized in that the setting change unit automatically changes the scan settings and then causes the image acquisition unit to reacquire image data.

7. The image processing system according to claim 6, characterized in that the setting change unit repeatedly performs a process to automatically change the scan settings and cause the image acquisition unit to reacquire image data until the image determination unit determines that no image defects exist.

8. The image processing system according to any one of claims 1 to 7, characterized in that the image determination unit determines whether or not an image defect exists in the image data by inputting the image data acquired by the image acquisition unit to a learning model that has learned images with image defects and images without image defects.

9. A learning model generation unit that generates the aforementioned learning model, Furthermore, The image processing system according to claim 8, characterized in that, if the learning model generation unit determines that there are no image defects in the image data reacquired by the image acquisition unit after the scan settings have been changed, it learns the image data with image defects before the scan settings were changed and the image data without image defects after the scan settings were changed, and updates the learning model.

10. An image acquisition step to obtain image data by scanning a document based on specified scan settings, An image determination step involves analyzing the image data acquired in the image acquisition step and determining whether or not there are image defects. If the image determination step determines that an image defect exists, the attribute determination step for determining the attributes of the image defect is performed. A presentation step in which the determination result from the attribute determination step is presented to the user, An image processing method characterized by having the following features.

11. A program executed in an image processing device that generates image data by scanning a document, wherein the image processing device is configured to: An image acquisition step to obtain image data by scanning a document based on specified scan settings, An image determination step involves analyzing the image data acquired in the image acquisition step and determining whether or not there are image defects. If the image determination step determines that an image defect exists, the attribute determination step for determining the attributes of the image defect is performed. A presentation step in which the determination result from the attribute determination step is presented to the user, A program characterized by causing the execution of a specific action.

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