Text defect detection method, computer device and storage medium

Abstract

The application provides a character defect detection method, a computer device and a storage medium, wherein the method comprises: acquiring a non-defect image and a to-be-detected image; performing first image processing on the non-defect image and the to-be-detected image respectively to obtain a first image of the non-defect image and a second image of the to-be-detected image; performing second image processing on the first image and the second image respectively to obtain a first contour image of each character in the first image and a second contour image of each character in the second image; matching the first contour image of each character and the second contour image; determining the similarity of the first contour image of each character and the matched second contour image; and determining the character detection result in the second contour image corresponding to the similarity according to the value of the similarity. The application can assist in character defect detection and improve the accuracy.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of optical defect detection, in particular to a text defect detection method, a computer device and a storage medium. BACKGROUND

[0002] In actual industrial production process, the text defect in printed image not only affects the appearance of product, but also misleads the user on the use performance of product, so it is necessary to detect the text defect of product to realize the quality control of product. The existing printed image text defect detection method based on Hu moments has too complex definition, which is not easy to explain the shape features of text in printed image, resulting in no clear processing direction when pre-processing sample image. SUMMARY

[0003] In view of the above, it is necessary to provide a text defect detection method, a computer device and a storage medium, which can reduce the false rejection rate and error rate of text defect detection through image pre-processing, and improve the accuracy and practicability of text defect detection.

[0004] The text defect detection method comprises: acquiring a non-defect image and a to-be-detected image; performing first image processing on the non-defect image and the to-be-detected image respectively to obtain a first image of the non-defect image and a second image of the to-be-detected image; performing second image processing on the first image and the second image respectively to obtain a first contour image of each text in the first image and a second contour image of each text in the second image; matching the first contour image and the second contour image of each text; determining the similarity of the first contour image of each text and the matched second contour image; and determining the text detection result in the second contour image corresponding to the similarity according to the value of the similarity.

[0005] Optionally, the first image processing comprises: performing image binarization according to a preset first threshold.

[0006] Optionally, the second image processing on the first image and the second image respectively to obtain the first contour image of each text in the first image and the second contour image of each text in the second image comprises: determining a first position of each text in the first image, segmenting each text in the first image according to the first position of each text and extracting the first contour image; and determining a second position of each text in the second image, segmenting each text in the second image according to the second position of each text and extracting the second contour image.

[0007] Optionally, the matching the first contour image and the second contour image of each character comprises: obtaining a first center point of the first contour image of each character and a second center point of the second contour image of each character; and matching the first contour image and the second contour image of the same character according to the first center point and the second center point.

[0008] Optionally, the determining the similarity of the first contour image and the matched second contour image of each character comprises: calculating a first Fourier descriptor of the first contour image of each character and a second Fourier descriptor of the second contour image of each character; and obtaining the similarity of the first contour image and the matched second contour image of each character according to the first Fourier descriptor of the first contour image and the second Fourier descriptor of the second contour image of the same character.

[0009] Optionally, the similarity comprises: a cosine distance of the first Fourier descriptor of the first contour image and the second Fourier descriptor of the second contour image of the same character.

[0010] Optionally, the similarity ranges from 0 to 2.

[0011] Optionally, the determining the character detection result in the second contour image corresponding to the similarity according to the value of the similarity comprises: comparing the value of the similarity with a preset second threshold; when the value of the similarity is greater than or equal to the preset second threshold, determining that the character in the second contour image corresponding to the similarity is a flawed character; and when the value of the similarity is less than the preset second threshold, determining that the character in the second contour image corresponding to the similarity is a non-flawed character.

[0012] The computer readable storage medium stores at least one instruction, and the at least one instruction is executed by the processor to implement the character flaw detection method.

[0013] The computer device comprises a memory and at least one processor, and the memory stores at least one instruction, and the at least one instruction is executed by the at least one processor to implement the character flaw detection method.

[0014] Compared with the prior art, the character flaw detection method, the computer device and the storage medium can detect flaws on the shape of characters by analyzing the contour shape of characters, and can reduce the false rejection rate and the false acceptance rate of detection through image preprocessing, and improve the accuracy and practicability of character flaw detection. BRIEF DESCRIPTION OF DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description only constitute a part of the embodiments of the present application, and for those skilled in the art, other drawings can be obtained based on the provided drawings without creative labor.

[0016] Figure 1 is a flow chart of the text defect detection method provided by the embodiments of the present application.

[0017] Figure 2 is an architecture diagram of the computer device provided by the embodiments of the present application.

[0018] Figure 3 is an example diagram of the defect-free image and the image to be detected provided by the embodiments of the present application.

[0019] Figure 4 is an example diagram of the first image and the second image provided by the embodiments of the present application.

[0020] Figure 5 is a flow chart in step S6 of the text defect detection method provided by the embodiments of the present application.

[0021] Main element symbol explanation

[0022] Computer device 3 Text defect detection system 30 Memory 31 Processor 32

[0023] The following specific embodiments will further illustrate the present application in combination with the above-mentioned drawings. DETAILED DESCRIPTION

[0024] In order to enable the above-mentioned purposes, features and advantages of the present application to be more clearly understood, the following will describe the present application in detail in combination with the drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0025] In the following description, many specific details are set forth in order to fully understand the present application. The described embodiments only constitute a part of the embodiments of the present application, and are not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0026] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs. The terms used in the specification of the present application are only for the purpose of describing the specific embodiments and are not intended to limit the present application.

[0027] Reference Figure 1As shown, a flowchart of the text defect detection method of the preferred embodiment of the present application.

[0028] In the present embodiment, the text defect detection method can be applied in a computer device (for example Figure 2 As shown in the computer device 3), for the computer device that needs to perform text defect detection, the method of the present application can be directly integrated on the computer device to provide the function for text defect detection, or run on the computer device in the form of a software development kit (SDK).

[0029] As shown, the text defect detection method specifically includes the following steps, and the order of the steps in the flowchart can be changed according to different requirements, and some steps can be omitted. Figure 1

[0030] Step S1, the computer device acquires a non-defective image and a to-be-detected image.

[0031] In one embodiment, the computer device can acquire a non-defective image and a to-be-detected image in response to user input. The non-defective image and the to-be-detected image can also be pre-stored in the memory of the computer device, or pre-stored in other devices in communication connection with the computer device.

[0032] In the present embodiment, the non-defective image can be a rectangular golden sample image of a certain printed matter produced by a factory, that is, the arrangement direction of the text in the non-defective image does not need to be corrected. In the present embodiment, the text contained in the non-defective image can refer to, for example, Chinese characters, numbers, English letters, etc.

[0033] In the present embodiment, the to-be-detected image can be a rectangular image taken for the certain printed matter to be detected.

[0034] In one embodiment, the to-be-detected image and the non-defective image are of the same size.

[0035] For example, referring to Figure 3 As shown, image 3A represents a non-defective image containing only one text, and image 3B represents a to-be-detected image.

[0036] Step S2, the computer device respectively performs first image processing on the non-defective image and the to-be-detected image to obtain a first image of the non-defective image and a second image of the to-be-detected image.

[0037] ​In one embodiment, the first image processing includes image binarization according to a preset first threshold. For example, when a pixel value at any position in the flawless image is greater than or equal to the first threshold (e.g., 30), the pixel at the position is binarized to 255; when the pixel value at any position in the flawless image is less than the first threshold, the pixel at the position is binarized to 0, obtaining a first image of the flawless image. Similarly, when a pixel value at any position in the image to be detected is greater than or equal to the first threshold (e.g., 30), the pixel at the position is binarized to 255; when the pixel value at any position in the image to be detected is less than the first threshold, the pixel at the position is binarized to 0, obtaining a second image of the image to be detected. For example Figure 4 As shown, image binarization is performed on the flawless image 3A to obtain an image 4A, and image binarization is performed on the image to be detected 3B to obtain an image 4B.

[0038] In other embodiments, the computer device can also determine the size of the first threshold by using the Otsu algorithm (OTSU Thresholding). It should be noted that the image binarization does not change the size of the image, and the size of the first image of the flawless image and the second image of the image to be detected is consistent.

[0039] Step S3, the computer device performs second image processing on the first image and the second image respectively, to obtain a first contour image of each character in the first image and a second contour image of each character in the second image.

[0040] In one embodiment, the computer device determines a first position of each character in the first image, segments each character in the first image according to the first position of each character and extracts a first contour image of each character; and determines a second position of each character in the second image, segments each character in the second image according to the second position of each character and extracts a second contour image of each character.

[0041] In one embodiment, the computer device can use optical character recognition (OCR) technology to recognize the characters in the first image and the second image, and then confirm the first position of each character in the first image and the second position of each character in the second image.

[0042] For example, the computer device can frame each character in the first image with a first rectangular frame in the first image, each character corresponding to a first rectangular frame, and each first rectangular frame containing a complete image of a character in the first image. In this embodiment, the first position of each character refers to the position of the first rectangular frame corresponding to the character in the first image. The computer device can take the lower left corner of the first image as a first coordinate origin o, and establish a first rectangular coordinate system xoy at the first coordinate origin o with the adjacent two edges of the first image as coordinate axes, thereby locating the first position of the first rectangular frame, for example, locating the lower left corner of the first rectangular frame at coordinate (x1, y1) of the first rectangular coordinate system and the upper right corner of the first rectangular frame at coordinate (x2, y2) of the first rectangular coordinate system, and determining the first position A by coordinate (x1, y1) and coordinate (x2, y2).

[0043] Similarly, the computer device can frame each character in the second image with a second rectangular frame in the second image, each character corresponding to a second rectangular frame, and each second rectangular frame containing a complete image of a character in the second image. In this embodiment, the second position of each character refers to the position of the second rectangular frame corresponding to the character in the second image. The computer device can take the lower left corner of the second image as a second coordinate origin O, and establish a second rectangular coordinate system XOY at the second coordinate origin O with the adjacent two edges of the second image as coordinate axes, thereby locating the second position of the second rectangular frame, for example, locating the lower left corner of the second rectangular frame at coordinate (X1, Y1) of the second rectangular coordinate system and the upper right corner of the second rectangular frame at coordinate (X2, Y2) of the second rectangular coordinate system, and determining the second position B by coordinate (X1, Y1) and coordinate (X2, Y2).

[0044] It should be noted that since the flawless image and the image to be detected are rectangular images taken for the same printed matter, and the size of the first image of the flawless image and the second image of the image to be detected is consistent, when the coordinates of the first position A and the second position B are the same, it can be determined that the character at the first position A and the character at the second position B are the same character, belong to the same position, and the first rectangular frame and the second rectangular frame of the same character are consistent in size.

[0045] In one embodiment, the computer device can segment and extract the first outline image of each character in the first image according to the first position of the each character using the character segmentation function of the OCR software; and segment and extract the second outline image of each character in the second image according to the second position of the each character using the character segmentation function of the OCR software.

[0046] For example, the computer device can segment each character in the first image along the edge of the first rectangular frame to obtain the first outline image of the each character. Similarly, each character in the second image can be segmented along the edge of the second rectangular frame to obtain the second outline image of the each character. It should be noted that the first outline image and the second outline image of the same character belonging to the same position are of the same size.

[0047] Step S4, the computer device matches the first outline image and the second outline image of each character.

[0048] In one embodiment, the matching of the first outline image and the second outline image of each character includes: obtaining a first center point of the first outline image of each character and a second center point of the second outline image of each character; and according to the first center point and the second center point, one-to-one corresponding the first outline image and the second outline image of the same character, wherein the same character refers to the character belonging to the same position in the flawless image and the image to be detected.

[0049] For example, the computer device can calculate the first center point and the second center point by using the OpenCV algorithm; calculate the coordinate (x3, y3) of the first center point in the first rectangular coordinate system and the coordinate (X3, Y3) of the second center point in the second rectangular coordinate system, and one-to-one corresponding the first center point and the second center point with the same coordinate, so as to one-to-one corresponding the first outline image and the second outline image of the same character belonging to the same position.

[0050] Step S5, the computer device determines the similarity of the first outline image and the matched second outline image of each character.

[0051] In one embodiment, the determining the similarity between the first contour image and the matched second contour image of each character comprises: calculating a first Fourier descriptor of the first contour image of each character and a second Fourier descriptor of the second contour image of each character, the first Fourier descriptor can be regarded as a multi-dimensional vector M, and the second Fourier descriptor can be regarded as a multi-dimensional vector N; obtaining the similarity between the first contour image and the matched second contour image of each character according to the first Fourier descriptor of the first contour image and the second Fourier descriptor of the second contour image belonging to the same character.

[0052] The similarity refers to a cosine distance dist(M, N) of the first Fourier descriptor of the first contour image and the second Fourier descriptor of the second contour image belonging to the same character, and the calculation formula is: dist(M, N) = 1-cos(M, N); the value range of the similarity is [0, 2].

[0053] It should be noted that the Fourier descriptor has translational invariance, rotational invariance, and scale invariance, and is not affected by the position, angle, and contour scaling of the character contour in the first contour image and the second contour image. For example, the value of the similarity between the character contour image in image 4A and the character contour image in image 4B is 0.06.

[0054] Step S6, the computer device determines the character detection result in the second contour image corresponding to the similarity according to the value of the similarity.

[0055] In one embodiment, the determining the character detection result in the second contour image corresponding to the similarity according to the value of the similarity comprises steps S60 to S62 as shown in the figure. Figure 5

[0056] Step S60, the computer device compares the value of the similarity with the size of the preset second threshold, when the value of the similarity is greater than or equal to the preset second threshold, step S61 is performed, and when the value of the similarity is less than the preset second threshold, step S62 is performed.

[0057] In one embodiment, the preset second threshold can be 0.05.

[0058] Step S61, the computer device determines that the character in the second contour image corresponding to the similarity is a flawed character.

[0059] ​In one embodiment, when the text in the second contour image corresponding to the similarity is flawed text, the text in the second contour image corresponding to the text in the image to be detected is flawed text. For example, the value of the similarity between image 4A and image 4B is 0.06, which is greater than the preset second threshold 0.05, so the text in image 4B is flawed text, i.e., the text in the image to be detected 3B is flawed text.

[0060] In step S62, the computer device determines that the text in the second contour image corresponding to the similarity is non-flawed text.

[0061] In one embodiment, when the text in the second contour image corresponding to the similarity is non-flawed text, the text in the second contour image corresponding to the text in the image to be detected is non-flawed text.

[0062] The above Figure 1 The text flaw detection method of the present application is described in detail below. Figure 2 The hardware device architecture for implementing the text flaw detection method is described.

[0063] It should be understood that the embodiments are only for illustration and do not limit the scope of the patent application.

[0064] Referring to Figure 2 FIG. 1 shows a structure schematic diagram of a computer device provided by the preferred embodiment of the present application. In the preferred embodiment of the present application, the computer device 3 includes a memory 31 and at least one processor 32. Those skilled in the art should understand that the computer device 3 can include more or fewer other hardware or software or different component arrangements. Figure 2 The structure of the computer device shown is not limited to the embodiments of the present application and can be a bus-type structure or a star-type structure. The computer device 3 can also include more or fewer other hardware or software or different component arrangements.

[0065] In some embodiments, the computer device 3 includes a terminal capable of automatically performing numerical calculation and / or information processing according to pre-set or stored instructions. The hardware thereof includes, but is not limited to, a microprocessor, an application-specific integrated circuit, a programmable gate array, a digital processor, and an embedded device.

[0066] It should be noted that the computer device 3 is only an example. Other existing or future electronic products, such as those adaptable to the present application, should also be included in the protection scope of the present application and are hereby incorporated by reference.

[0067] In some embodiments, the memory 31 is configured to store program codes and various data. For example, the memory 31 can be configured to store the flawless image and the image to be detected, and can also store the text defect detection system 30 installed in the computer device 3, and to realize high-speed and automatic access of programs or data during the operation of the computer device 3. The memory 31 includes a Read-Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Programmable Read-Only Memory (EPROM), a One-time Programmable Read-Only Memory (OTPROM), an Electrically-Erasable Programmable Read-Only Memory (EEPROM), a Compact Disc Read-Only Memory (CD-ROM) or other optical disk memories, a magnetic disk memory, a magnetic tape memory, or any other computer-readable storage medium capable of carrying or storing data.

[0068] In some embodiments, the at least one processor 32 can be composed of integrated circuits, for example, can be composed of a single packaged integrated circuit, or can be composed of multiple packaged integrated circuits with the same function or different functions, including one or more combinations of a Central Processing unit (CPU), a microprocessor, a digital processing chip, a graphics processor, and various control chips. The at least one processor 32 is the control core of the computer device 3, and is connected to various components of the computer device 3 through various interfaces and lines, and executes or runs the programs or modules stored in the memory 31 and calls the data stored in the memory 31, to perform various functions and process data of the computer device 3, for example, to perform the function of text defect detection.

[0069] In some embodiments, the text defect detection system 30 is operated in the computer device 3. The text defect detection system 30 can include multiple functional modules composed of program code segments. The program codes of various program segments in the text defect detection system 30 can be stored in the memory 31 of the computer device 3 and executed by the at least one processor 32, to realize Figure 1 the function of text defect detection.

[0070] In the embodiment, the text defect detection system 30 can be divided into a plurality of functional modules according to the functions performed by the text defect detection system 30. The module referred to in the present application refers to a series of computer program segments capable of being executed by at least one processor and capable of completing a fixed function, which are stored in the memory.

[0071] Although not shown, the computer device 3 can further include a power supply (such as a battery) for powering the various components. Preferably, the power supply can be logically connected to the at least one processor 32 through a power management device, so that the power management device can realize the functions of managing charging, discharging, and power consumption management, etc. The power supply can also include one or more direct current or alternating current power supplies, recharging devices, power failure detection circuits, power converters or inverters, power status indicators, etc. The computer device 3 can also include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be described here.

[0072] It should be understood that the embodiments are only for illustration and are not limited in the scope of the patent application by the structure.

[0073] The integrated units in the form of software functional modules described above can be stored in a computer readable storage medium. The software functional modules described above are stored in a storage medium, including a plurality of instructions for causing a computer device (which can be a server, a personal computer, etc.) or a processor to execute part of the method described in each embodiment of the present application.

[0074] In further embodiments, in combination Figure 2 , the at least one processor 32 can execute the operating system of the computer device 3 and various installed application programs (such as the text defect detection system 30), program codes, etc., such as the various modules described above.

[0075] The memory 31 stores program codes, and the at least one processor 32 can call the program codes stored in the memory 31 to execute related functions. The program codes stored in the memory 31 can be executed by the at least one processor 32, thereby realizing the functions of the various modules to achieve the purpose of text defect detection.

[0076] In an embodiment of the present application, the memory 31 stores one or more instructions (i.e., at least one instruction) which are executed by the at least one processor 32 to realize Figure 1 the purpose of text defect detection shown.

[0077] In several embodiments provided in the present application, it should be understood that the disclosed apparatus and method can be implemented by other manners. For example, the apparatus embodiments described above are merely illustrative, for example, the division of the modules is merely a logical function division, and actual implementation can have another division manner.

[0078] The modules described as separate components can or can not be physically separated, and the components shown as modules can or can not be physical units, that is, can be located in one place, or can be distributed to multiple network units. Part or all of the modules can be selected according to actual needs to achieve the purpose of the embodiment.

[0079] In addition, the function modules in each embodiment of the present application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of hardware plus software function modules.

[0080] It is obvious for those skilled in the art that the present application is not limited to the details of the above exemplary embodiments, and the present application can be implemented in other specific forms without departing from the spirit or essential characteristics of the present application. Therefore, the embodiments should be regarded as exemplary and non-limiting, and the scope of the present application is defined by the appended claims rather than the above description, and all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be included in the present application. Any reference signs in the claims should not be regarded as limiting the claims. In addition, it is obvious that the word "comprise" does not exclude other units or, singular does not exclude plural. The plurality of units or devices stated in the device claims can also be implemented by one unit or device through software or hardware. The words first, second, etc. are used to indicate names, and do not indicate any specific order.

[0081] Finally, it should be noted that the above embodiments are merely used to illustrate the technical solutions of the present application and not to limit the present application. Although the present application has been described in detail with reference to the above preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present application.

Claims

1. A method for detecting text defects, characterized in that, The method includes: Acquire flawless images and images to be inspected; The flawless image and the image to be detected are respectively subjected to a first image processing to obtain a first image of the flawless image and a second image of the image to be detected; The first image and the second image are processed by the second image respectively to obtain the first outline image of each character in the first image and the second outline image of each character in the second image; Matching the first contour image and the second contour image of each character includes: obtaining the first center point of the first contour image of each character and the second center point of the second contour image of each character; and matching the first contour image and the second contour image belonging to the same character one by one based on the first center point and the second center point. Determining the similarity between the first contour image and the matching second contour image of each character includes: calculating the first Fourier descriptor of the first contour image of each character and the second Fourier descriptor of the second contour image of each character; obtaining the similarity between the first contour image and the matching second contour image of each character based on the first Fourier descriptor of the first contour image and the second Fourier descriptor of the second contour image belonging to the same character. Based on the similarity value, the text detection result in the second contour image corresponding to the similarity value is determined.

2. The text defect detection method according to claim 1, characterized in that, The first image processing includes: performing image binarization according to a preset first threshold.

3. The text defect detection method according to claim 1, characterized in that, The step of performing second image processing on the first image and the second image respectively to obtain a first contour image of each character in the first image and a second contour image of each character in the second image includes: Determine the first position of each character in the first image, segment each character in the first image according to the first position of each character, and extract the first contour image; and Determine the second position of each character in the second image, segment each character in the second image according to the second position of each character, and extract the second contour image.

4. The text defect detection method according to claim 1, characterized in that, The similarity includes: The cosine distance between the first Fourier descriptor of the first contour image and the second Fourier descriptor of the second contour image, which belong to the same character.

5. The text defect detection method according to claim 1, characterized in that, The similarity value ranges from [0, 2].

6. The text defect detection method according to claim 1, characterized in that, The step of determining the text detection result in the second contour image corresponding to the similarity value includes: Compare the similarity value with a preset second threshold. When the similarity value is greater than or equal to the preset second threshold, the text in the second contour image corresponding to the similarity is determined to be flawed text. When the similarity value is less than the preset second threshold, the text in the second contour image corresponding to the similarity is determined to be flawless text.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one instruction, which, when executed by a processor, implements the text defect detection method as described in any one of claims 1 to 6.

8. A computer device, characterized in that, The computer device includes a memory and at least one processor, the memory storing at least one instruction which, when executed by the at least one processor, implements the text defect detection method as described in any one of claims 1 to 6.

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