A machine vision-based graphics printing quality analysis system and method

Abstract

The application discloses a kind of based on machine vision's graphic printing quality analysis system and method, belong to machine vision and graphic detection technical field.Method includes steps S100: obtaining the graphic data to be analyzed, the graphic data includes N target page {Page1, Page2, …, PageN};N>2;S200: for each target page Pagei, i ∈ {1,2, …, N}, determine corresponding printing quality analysis method, the quality analysis method includes only text printing quality analysis or text-image associated printing quality analysis;S300: when target page Pagei exists printing quality problem, output target page Pagei;The printing quality problem includes one of the following types or its combination: character printing error, image printing error, character-image associated error.System is used to realize the method.The technical scheme of the application can be based on machine vision Maximum limit discovers graphic printing error especially graphic associated printing error.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of machine vision and graphic text detection, and particularly relates to a graphic text printing quality analysis system and method based on machine vision, an electronic device implementing the method, a computer readable storage medium, and a computer program product. BACKGROUND

[0002] In the printing industry, the necessity of graphic text printing quality detection is increasingly prominent, and it runs through multiple key aspects such as production, market, and enterprise development. From the perspective of production, accurate quality detection can greatly improve production efficiency. In traditional printing production, once quality problems such as blurred text and image color deviation occur, it may lead to a large number of products becoming waste products, causing waste of materials, time, and labor. Through quality detection, the printing process can be monitored in real time, and once a problem is found, the printing equipment parameters or process can be adjusted immediately to avoid the problem from being magnified. Standardized quality detection can ensure that the printed matter flowing into the market meets the relevant standards, protect the reading experience and use rights of consumers, and at the same time, maintain the interests of the legitimate printing enterprises, and promote the healthy development of the market. On the contrary, if an enterprise ignores quality detection and reduces the quality detection link, the quality of the printed matter will be unstable, and customer complaints will be frequent, which will eventually seriously affect the reputation of the enterprise, lose customer resources, and hinder the sustainable development of the enterprise.

[0003] In related technologies, graphic text printing quality detection technology mainly includes detection technology based on optical principles, machine vision and image processing technology, digital and intelligent analysis technology, etc. Among them, machine vision and image processing technology can extract features related to printing quality from pre-processed images, such as the thickness of text strokes, image texture features, and geometric shapes of graphics. Using edge detection algorithms such as Sobel operator and Canny operator, edge information in the image can be extracted to determine whether the lines are clear and the overprint is accurate. For complex graphic text patterns, machine learning algorithms such as support vector machine (SVM) and convolutional neural network (CNN) are used to train a large number of printed images with quality annotations, so that the model learns the feature patterns under different quality conditions. The trained model can classify new printed images to determine whether there are quality problems and the type of problems, such as whether there are ink stains, missing prints, and ghosting defects. For example, the error detection system and method for book printing proposed in Chinese patent CN119180972A, the object system based on monitoring printed graphic text detection proposed in Chinese patent CN117576031A, and the image inspection filter proposed in CN114202498A.

[0004] The prior art for the monitoring of graphic-text printing errors is mostly limited to text errors and image errors themselves. However, with the development and improvement of related quality monitoring technologies, such simple errors have been greatly reduced. Subsequently, graphic-text associated printing errors, such as text and pictures themselves are clearly visible, but the text and pictures themselves are not associated or the associated position is incorrect. For such errors, the related prior art does not mention, and no relevant automated solutions are found. SUMMARY

[0005] To solve the above technical problems, the present application proposes a graphic-text printing quality analysis system and method based on machine vision, an electronic device implementing the method, a computer readable storage medium, and a computer program product.

[0006] In a first aspect of the present application, a graphic-text printing quality analysis method based on machine vision is proposed, the method comprising the following steps:

[0007] S100: obtaining graphic-text data to be analyzed, the graphic-text data comprising N target pages {Page1, Page2, …, PageN}; N>2;

[0008] S200: determining a corresponding printing quality analysis method for each target page Pagei, i∈{1, 2, …, N}, the quality analysis method comprising only text printing quality analysis or text-image associated printing quality analysis;

[0009] S300: outputting the target page Pagei when there is a printing quality problem in the target page Pagei; the printing quality problem comprising one of the following types or a combination thereof:

[0010] Text printing error, image printing error, text-image associated error;

[0011] Wherein, step S200 specifically comprises:

[0012] S210: obtaining the previous page Pageup and the next page Pagedown of the target page Pagei as a set of pages to be analyzed;

[0013] S220: determining the printing quality analysis method of the target page Pagei as only text printing quality analysis based on machine vision when all elements of the set of pages to be analyzed do not have images or image descriptions;

[0014] Otherwise, determining the printing quality analysis method of the target page Pagei as text-image associated printing quality analysis.

[0015] After the step S210 and before the step S220, the method further comprises:

[0016] S211: text semantic recognition is performed on each element in the set of pages to be analyzed to determine whether there is an image description;

[0017] S212: if there is no image description for each element, image feature recognition is continued to be performed on each element to determine whether there is an image.

[0018] When the step S220 identifies that there is a first image for an element in the set of pages to be analyzed, image semantic recognition is performed on the first element to obtain a first image semantic recognition result;

[0019] Text semantic recognition is performed on each element in the set of pages to be analyzed to obtain a second text semantic recognition result;

[0020] Based on the matching association result of the first image semantic recognition result and the second text semantic recognition result, it is determined whether the target page Pagei has a printing quality problem.

[0021] The step S211 specifically includes:

[0022] If there is an image description for an element in the set of pages to be analyzed, a target image page is determined based on the image description, the target image page is fused with the set of pages to be analyzed, and an updated set of pages to be analyzed is obtained.

[0023] The first aspect of the method for analyzing the printing quality of images and texts based on machine vision can be automatically executed through computer program instructions. Therefore, in the second aspect of the present application, a method for analyzing the printing quality of images and texts based on machine vision is provided, which is automatically implemented based on a computer program. The method includes the following computer program flow steps (pseudo code):

[0024] Step 1: Obtain the image and text data to be analyzed, which includes N target pages {Page1, Page2, …, PageN}; N>2; let i=2;

[0025] Step 2: Obtain {Pagei-1, Pagei, Pagei+1} as an initial set of pages to be analyzed Pageset0;

[0026] Step 3: Perform text semantic recognition on Pagei-1, Pagei, and Pagei+1 in turn to determine whether there is an image description;

[0027] If there is an image description, a target image page is determined based on the image description, the target image page is fused with the set of pages to be analyzed, an updated set of pages to be analyzed Pageset1 is obtained, and step 7 is entered;

[0028] If the initial set of pages to be analyzed Pageset0 does not have image description, go to step 4;

[0029] Step 4: Image feature recognition is performed on the initial set of pages to be analyzed Pageset0, and it is determined whether there is an image;

[0030] If yes, output the text-image association error;

[0031] If no, go to step 5;

[0032] Step 5: Based on the text semantic recognition result of step 3, it is determined whether there is a text printing error; let i = i + 3;

[0033] Step 6: It is determined whether i > N is true, if yes, end; otherwise, return to step 2;

[0034] Step 7: The text semantic recognition result and the image semantic recognition result of each element in the updated set of pages to be analyzed Pageset1 are obtained;

[0035] Step 8: Based on the matching association result of the image semantic recognition result and the text semantic recognition result, it is determined whether there is a printing quality problem; let i = i + 1, and return to step 6.

[0036] To realize the above method, in a third aspect of the present application, a graphic-text printing quality analysis system based on machine vision is also provided, which comprises:

[0037] A graphic-text data acquisition unit: acquiring graphic-text data to be analyzed, the graphic-text data comprising N target pages {Page1, Page2, …, PageN}; N > 2;

[0038] A machine vision analysis unit: for each target page Pagei, i ∈ {1, 2, …, N}, determining a corresponding printing quality analysis method, the quality analysis method comprising only text printing quality analysis or text-image association printing quality analysis;

[0039] A quality analysis unit: performing graphic-text printing quality analysis based on the corresponding printing quality analysis method of each target page Pagei, and sending the analysis result to an output unit;

[0040] The analysis result comprises outputting the target page Pagei when the target page Pagei has a printing quality problem;

[0041] A correction unit: correcting the printing quality problem of the output target page;

[0042] The printing quality problem includes one of the following types or a combination thereof: a character printing error, an image printing error, a character-image association error;

[0043] The quality analysis unit determines a corresponding printing quality analysis method for each target page Pagei, i∈{1, 2, …, N}, specifically including:

[0044] Obtain the previous page Pageup and the next page Pagedown of the target page Pagei as a set of pages to be analyzed;

[0045] When all elements of the set of pages to be analyzed do not have images or image descriptions, determine that the printing quality analysis method of the target page Pagei is only text printing quality analysis based on machine vision; otherwise, determine that the printing quality analysis method of the target page Pagei is text-image association printing quality analysis.

[0046] The character printing error includes a wrong character, a garbled code, or a combination thereof;

[0047] The image printing error includes image blurring, image sequence number error, or a combination thereof.

[0048] In a fourth aspect of the present application, an electronic device is also provided, which includes a processor and a memory; the memory is used to store computer program code; when the program code is executed by the processor, the aforementioned machine vision-based printing quality analysis method is implemented.

[0049] In a fifth aspect of the present application, a computer readable storage medium is also provided, which stores computer program instructions, and the computer program is executed by an electronic device including a processor to implement the aforementioned machine vision-based printing quality analysis method.

[0050] The technical solution of the present application can further detect text-image association errors on the basis of detecting existing basic printing errors (character error / blurring, image error / blurring), i.e., the present application first shifts the focus to text-image printing quality analysis and monitoring related to text-image semantic association, realizing the leap from "pixel-level detection" to "semantic-level understanding".

[0051] The specific advantages and implementation principles of the technical solution of the present application will be further embodied in detail in the specific embodiment part in combination with the drawings of the specification. BRIEF DESCRIPTION OF DRAWINGS

[0052] 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. Obviously, the drawings described below only constitute some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained from these drawings without creative labor.

[0053] Figure 1 is a main flowchart of a machine vision-based graphic-text printing quality analysis method according to an embodiment of the present application

[0054] Figure 2 is Figure 1 is a further preferred embodiment of the method

[0055] Figure 3 is a program flowchart of a machine vision-based graphic-text printing quality analysis method implemented by computer program code

[0056] Figure 4 is a functional module composition diagram of a system embodiment of the machine vision-based graphic-text printing quality analysis method DETAILED DESCRIPTION

[0057] In the detailed description of the present application, if the embodiments of the related technical solutions involve user-related data, when the embodiments of the present application are applied to specific products or technologies, the user's permission or consent is required, and the collection, use and processing of the related data need to comply with the relevant laws, regulations and standards of the country and region.

[0058] Before introducing the specific embodiments of the present application, first introduce the status and defects of the related prior art, so as to lead to the technical problems to be solved by the present application, and then preliminarily develop the technical solutions of the present application, so that the technical personnel in the art can better understand the advantages and effects of the present application.

[0059] In the field of graphic-text printing, text errors and image errors are two common and important problems, which will be described in detail as follows.

[0060] Text errors

[0061] Spelling and grammar errors: This is the most basic and easily found type of typographical error. During the typesetting process, due to human negligence or lack of rigorous review of the manuscript, word spelling errors often occur. For example, "again and again" is mistakenly written as "again and again", "outline" is mistakenly written as "topic outline", etc. Such errors seriously affect the accuracy and professionalism of the text, especially in books, newspapers and other printed matter with high requirements for text quality, which greatly reduces the reading experience of readers and may even lead to information transmission errors. Grammar errors cannot be ignored, such as incomplete sentence components, improper collocation, and chaotic order, for example, "Through this activity, I realized the importance of unity", which uses the preposition improperly, resulting in a missing subject. Such errors will make the text sentence not smooth, the logic expression not clear, and thus weaken the persuasiveness of the content.

[0062] Font and size errors: Different printing scenarios have specific requirements for font and size. If not chosen properly, it will destroy the overall layout aesthetics and reading convenience. In formal document printing, if the standard Songti font is mistakenly used as a cartoon font, it will look extremely informal and affect the seriousness of the document. In terms of font size, if the text and title font sizes are not clearly distinguished, or the font sizes within a paragraph are inconsistent, it will be difficult for readers to quickly distinguish the text hierarchy, increasing the difficulty of reading. For example, in a promotional poster, if the title font size is too small, it cannot attract the attention of the audience, and thus cannot effectively convey key information, reducing the promotional effect of the poster.

[0063] Fuzzy and ghosted text: This is usually related to printing equipment and technology. Inadequate precision of printing equipment, such as clogged printer nozzles, worn printing rollers, etc., will make the text edges unclear, resulting in fuzzy phenomenon. In high-speed printing process, if the ink drying speed is too slow, the paper will shift slightly during transmission, which will easily cause text ghosting. Text fuzziness and ghosting not only affect the visual effect, but also make it difficult to read the text, which will cause serious information interpretation barriers for printed matter that mainly convey information through text, such as instruction manuals, contracts, etc., which may lead to potential disputes or misunderstandings.

[0064] Omission and duplication of words: Errors in manuscript editing, typesetting or printing may result in missing or extra words. In book printing, missing words may change the meaning of the sentence or even make it incomprehensible, such as "he went to the library to read" missing to "he went to the library to read", which makes the semantics ambiguous. Extra words will make the sentence redundant and logic confused, such as "I like this book very much", "very" and "very" repeat the semantics, affecting the fluency and accuracy of expression, and reducing the quality of printed matter.

[0065] Image errors

[0066] Image blur and distortion: Low resolution of the image itself is a common cause of blur. If the image material used for printing is a low-resolution picture downloaded from the network at will, when enlarged for printing, the pixels are stretched, and obvious jaggedness and blur will occur. During image scanning or shooting, poor device performance, improper operation (such as hand shaking, inaccurate focusing), etc. can also cause image blur. Image distortion is manifested as color deviation, shape deformation, etc. Improper color management, such as inconsistency of color space of different devices (camera, display, printer), without correct color calibration, will cause the printed image color to have a large difference from the original image. During image editing, overstretching and distorting the image will cause the image shape to be distorted, which cannot truly restore the original scene or object shape, affecting the visual appeal and information transmission effect of the printed matter.

[0067] Image misregistration: In multi-color printing, different color image layers are printed in sequence. If the precision of the registration system of the printing equipment is not enough, or the paper is stretched and deformed during plate making and printing, the problem of image misregistration will occur. Common manifestations are different color misregistration lines at the edges of the image, such as red, yellow, blue, etc. in color packaging box printing, the misregistration of different colors at the outline of the pattern makes the pattern look unclear and incomplete, which seriously affects the appearance quality and brand image of the product.

[0068] Image printing defects: including ink dots, scratches, blank areas, etc. Ink dots may be due to ink impurities, incomplete cleaning of the printing equipment, etc. and adhere to the surface of the image during printing to form small black dots, affecting the appearance of the image. Scratches may be caused by hard objects scratching the image during image transmission, plate making or printing, resulting in linear damage to the image. The blank area may be caused by insufficient ink transfer, insufficient printing pressure, etc. resulting in no ink covering the image in some areas, which appears blank. These defects will damage the integrity and aesthetics of the image and reduce the quality level of the printed matter.

[0069] It can be understood that the above text errors and image errors are errors caused by the text and image themselves, and are not related to position and relevance.

[0070] For the above errors caused by the text and image themselves, through relevant text and image recognition technologies, especially text semantic recognition technology and image semantic recognition technology, combined with natural language processing technology (NLP) and image feature extraction technology, by training relevant deep learning models and neural network models, the existing technology can automatically identify the above basic types of errors. With the development and improvement of related quality monitoring technologies, such simple errors have been greatly reduced.

[0071] However, the inventors have found in practical applications that even if the relevant text and images are correct, for example, the text is printed clearly, the semantics are clear, and the images are not missing or incorrect, there can still be quality problems, the most typical of which is text-image association errors, including text-image mismatch, text without images (which should actually have images), images without text (which should actually have correct text), and text-image position errors.

[0072] Accordingly, the corresponding technical solutions of the present application are proposed.

[0073] Firstly, referring to Figure 1 , Figure 1 is a main flowchart of a text-image printing quality analysis method based on machine vision according to an embodiment of the present application.

[0074] Figure 1 The method comprises steps S100-S300, which are implemented as follows:

[0075] S100: Obtain text-image materials to be analyzed, which include N target pages {Page1, Page2, …, PageN}; N>2;

[0076] S200: For each target page Pagei, i∈{1, 2, …, N}, determine the corresponding printing quality analysis method, which includes text-only printing quality analysis or text-image association printing quality analysis;

[0077] S300: When the target page Pagei has a printing quality problem, output the target page Pagei; the printing quality problem includes one or a combination of the following types:

[0078] Text printing errors, image printing errors, and text-image association errors.

[0079] Next, the steps of the above method embodiment will be introduced in detail. Figures 2-3

[0080] Firstly, step S100 obtains text-image materials to be analyzed.

[0081] For example, the text-image materials can be text-image electronic manuscripts to be delivered for printing, or initial paper manuscripts after the initial printing / scanning of the text-image electronic manuscripts. Both of the above are referred to as sample manuscripts. The sample manuscripts are manuscript materials before formal printing and publishing, which need to pass the printing quality analysis before being formally published and printed.

[0082] The meaning of text-image materials means that the sample manuscripts contain text and images.

[0083] ​For the convenience of description, it is assumed that the graphic material sample is an N-page book, i.e., the graphic material includes N target pages {Page1, Page2, …, PageN}; N>2; each target page can contain only text, only display pictures, or a combination of text and pictures.

[0084] Next, step S200 is entered: for each target page Pagei, i∈{1, 2, …, N}, a corresponding print quality analysis method is determined, which includes only text print quality analysis or text-image association print quality analysis;

[0085] The print quality analysis method of the present application includes two types: (only) text print quality analysis method or text-image association print quality analysis method.

[0086] The former (text print quality analysis method) can be considered as the prior art, i.e., based on related text semantic recognition technology, natural language processing technology, large language model technology, etc. to recognize errors existing in the text, including wrong characters, missing characters, semantic errors, font size errors, font errors, blurring, ghosting, multiple characters, etc., i.e., "pixel-level detection".

[0087] The latter (text-image association print quality analysis) is the improvement focus of the present application, i.e., "semantic-level understanding".

[0088] Another focus of the present application is when to perform text print quality analysis only by "pixel-level detection" to identify text errors, and when to perform "semantic-level understanding" to realize text-image association print quality analysis. Because the resources required by the former are significantly less than those required by the latter, if only the former is performed, relevant associated semantic errors will be missed (equivalent to only realizing the prior art); but if all target pages perform the latter "semantic-level" text-image association print quality analysis, it is unnecessary and will result in a large waste of resources and reduce the identification efficiency.

[0089] The timing selection of step S200 will be further described in subsequent embodiments.

[0090] After step S200, step S300 is entered: when the target page Pagei has a print quality problem, the target page Pagei is output; the print quality problem includes one or a combination of the following types: text printing error, image printing error, text-image association error.

[0091] It can be understood that the text printing error can be obtained by performing the only text printing quality analysis method, and the image printing error and the text-image association error are obtained in the text-image association printing quality analysis process. Of course, the text-image association printing quality analysis process can also identify the text printing error at the same time, and the specific principle will be shown in the subsequent examples.

[0092] Further referring to Figure 2 , Figure 2 is Figure 1 The further preferred embodiment of the method is schematically shown, which further expands the step S200 of the method. Figure 1 The further preferred embodiment of the method is schematically shown, which further expands the step S200 of the method.

[0093] Specifically, the step S200 specifically includes:

[0094] S210: obtaining the previous page Pageup and the next page Pagedown of the target page Pagei as a set of pages to be analyzed;

[0095] S220: when all elements of the set of pages to be analyzed do not exist image or image description, determining that the printing quality analysis method of the target page Pagei is only text printing quality analysis based on machine vision;

[0096] Otherwise, it is determined that the printing quality analysis method of the target page Pagei is text-image association printing quality analysis.

[0097] In one specific example, the N target pages are the first page, the second page, …, and the Nth page in turn, corresponding to {Page1, Page2, …, PageN} respectively;

[0098] The traditional printing quality analysis method is in units of pages, and each page is analyzed one by one. This is because the traditional printing quality analysis method is for text or image itself, without considering the association before and after, so only single-page analysis is needed.

[0099] However, the technical solution of the present application focuses on the monitoring of text-image association errors, so it cannot be based on only a single page, and the association before and after and the context association must be considered.

[0100] Therefore, in step S210, for each target page Pagei, the previous page Pageup and the next page Pagedown of the target page Pagei need to be obtained as a set of pages to be analyzed;

[0101] That is, the printing quality analysis of the present application is in units of a set of pages to be analyzed.

[0102] The page before the target page Pagei, Pageup, and the page after the target page Pagei, Pagedown, can be the previous and next pages of the current target page.

[0103] As an example (A), the page before the target page Pagei, Pageup, and the page after the target page Pagei, Pagedown, can be the previous and next pages of the current target page; at this time, the set of pages to be analyzed has three elements in total.

[0104] As another example (B), the page before the target page Pagei, Pageup, and the page after the target page Pagei, Pagedown, can be the previous and next pages of the current target page; at this time, the set of pages to be analyzed has (2k+1) elements in total.

[0105] According to actual statistical data, if there is a description of image-text association (image description), the relevant image-text positions generally do not exceed three pages.

[0106] Such illustrative examples of image-text description include:

[0107] (1) “See the following figure……”, “See the previous figure……”, “As shown in the above figure”, “As shown in the following figure”, “Legend……”, “Diagram……”, “Appendix……”;

[0108] (2) “See the following page figure”:

[0109] (3) “See the previous page figure”;

[0110] For (1), it generally means that the relevant image should appear near the corresponding text description, for example, at any position above, in the middle, or below the current page;

[0111] For (2) and (3), the text description explicitly indicates that the relevant figure should appear on the next page or the previous page.

[0112] Therefore, considering most cases, the embodiments of the present application will be introduced based on the above example (A), i.e., the page before the target page Pagei, Pageup, and the page after the target page Pagei, Pagedown, are the previous and next pages of the current target page, and the set of pages to be analyzed has three elements in total.

[0113] Of course, it can be understood that the technical solution of the present application can also be the case of the above example (B), and the case of example (B) can theoretically summarize all cases (for example, k approaches N) based on the increase of k, but it can be predicted that the range and resource calling amount of each monitoring will also increase sharply. In the case of considering cost control and efficiency and quality targets, the value of k needs to be controlled. Preferably, k can be equal to 1, 2, 3, 4, 5, or k<(N / k).

[0114] Of course, when the target page is the first page, the target page will not have a previous 1 page (previous several pages); when the target page is the last page, the target page will not have a next 1 page (next several pages).

[0115] When the method is automatically implemented based on a computer program, the above description can be unified by setting a blank page element, for example, when the target page is the first page, the previous page Pageup of the target page Pagei is considered to be a blank page (NULL1); when the target page is the last page, the next page Pagedown of the target page Pagei is considered to be a blank page (NULL2), so as to ensure that for each target page, the situation that the set of pages to be analyzed has three elements in common can be obtained.

[0116] Alternatively, when the target page is the first page, the set of pages to be analyzed is set as {page1, page2, page3}; when the target page is the last page, the set of target pages to be analyzed is set as {pageN-2, pageN-1, pageN}, and the like. Such processing can realize special settings for special situations on a computer program, without affecting the implementation integrity of the entire program.

[0117] For ease of description, the following embodiments all assume that the target page is not the first page or the last page, but a middle page.

[0118] Next, step S220 is performed: when all elements of the set of pages to be analyzed do not have images or image descriptions, it is determined that the print quality analysis method of the target page Pagei is only text print quality analysis based on machine vision;

[0119] Otherwise, it is determined that the print quality analysis method of the target page Pagei is text-image association print quality analysis.

[0120] In one situation, if all elements (three consecutive pages) of the set of pages to be analyzed do not have images or image descriptions (whether explicit or implicit), it means that the three consecutive pages are only text descriptions and do not involve any text-image association. At this time, only text print quality analysis is needed, that is, based on machine vision technology combined with natural language analysis technology, text printing errors of the text itself are identified, including one or any combination of the text printing errors, such as garbled code, wrong characters, missing characters, semantic errors, font size errors, and font errors, that is, “pixel-level detection”.

[0121] Identifying whether a target page has an image belongs to simple prior art, and the present application does not expand on this.

[0122] Identifying whether a target page has an image description can also be realized based on prior art.

[0123] For example, the presence of an obvious image description can be determined based on keyword (key sentence) recognition, such as the aforementioned illustrative image-text description examples (1)-(3).

[0124] In some extreme cases, it can be too single to determine the presence of an image description only by text semantic recognition. Some image-related information can not be directly presented in the text in the form of an explicit "image description", but can be represented by the context, or some specific symbols, signs, etc. to indicate the presence of an image or related information, i.e. implied description.

[0125] First of all, it needs to be understood that no technical solution can handle all cases, and the accuracy cannot reach 100%. Related technical solutions are auxiliary technologies that need to take into account the improvement of computational complexity, spatial complexity and work efficiency; secondly, as long as the technical solution can handle most cases, it means that the solution has feasibility and generalizability. For some special cases or extreme cases, machine learning technology can be used for continuous improvement, for example, in addition to text semantic recognition, some common image-related signs and symbols need to be recognized, and more in-depth analysis needs to be combined with the context to more comprehensively determine whether there is image-related information. This can be achieved by continuously updating the large language model through semantic database iteration.

[0126] In addition to such cases (all elements in the set of pages to be analyzed do not exist images or image descriptions), other cases, for example, an element exists an image, or, an element exists an image description, then the corresponding text-image association printing quality analysis method needs to be performed.

[0127] Figure 2 In the embodiment of the application, after the step S210, before the step S220, the method further comprises:

[0128] S211: performing text semantic recognition on each element in the set of pages to be analyzed to determine whether an image description exists;

[0129] S212: if each element does not exist an image description, then continue to perform image feature recognition on each element to determine whether an image exists.

[0130] Specifically, if each element does not exist an image description, but at least one element exists an image, it is more likely to mean that there is a text-image association error.

[0131] Because, if each element in the set of target pages to be analyzed does not exist an image description, it should be that none of the three consecutive pages exists an image (no image should appear).

[0132] In another aspect, when the step S220 identifies that a first image exists for a certain element in the set of pages to be analyzed, image semantic recognition is performed on the first element to obtain a first image semantic recognition result;

[0133] Text semantic recognition is performed on each element in the set of pages to be analyzed to obtain a second text semantic recognition result;

[0134] Based on the matching association result of the first image semantic recognition result and the second text semantic recognition result, it is determined whether the target page Pagei has a printing quality problem.

[0135] In this case, a first element in the set of target pages to be analyzed has an image, i.e., an image appears in a certain page, and under normal circumstances, a similar text description of the image should exist near the image.

[0136] At this time, image semantic feature recognition can be performed on the first element based on an image semantic recognition model to obtain a first image semantic recognition result, i.e., at least one of content features, texture features, pixel features, and author information of the image is recognized;

[0137] Then, text semantic recognition is performed on each element in the set of pages to be analyzed based on a text semantic recognition model to obtain a second text semantic recognition result, i.e., a description content related to the image semantic features is recognized;

[0138] Under normal circumstances, the second text semantic recognition result should contain description content matching at least one of the content features, texture features, and pixel features of the aforementioned image;

[0139] For example, the first image semantic recognition result is a landscape painting, and the second text semantic recognition result should contain content related to the description of the landscape painting; if the two match, the target page Pagei does not have a printing quality problem;

[0140] For another example, the first image semantic recognition result is a black engineering bearing, and the second text semantic recognition result should contain content related to the engineering bearing; if the second text semantic recognition result is all about introducing a blue valve, the two do not match, and the target page Pagei has a printing quality problem, specifically, a text-image association error.

[0141] Text-image association error: It is possible that the image material itself has errors, such as shooting the wrong scene, characters, or mistakenly deleting or modifying key elements during image editing. In advertising printing, if the product picture does not match the actual promoted product, it will mislead consumers and damage the reputation of the enterprise. It is also a common problem that the image content does not match the text information, such as in popular science books, the pictures do not correspond to the knowledge points described in the text, which cannot play the role of auxiliary explanation, affecting the understanding and absorption of knowledge by readers.

[0142] Of course, there may be a situation where the first image semantic recognition result cannot identify any specific content, representing that the image itself is blurred, low in clarity, or missing, etc. At this time, without the second text semantic recognition result, it can be determined that the target page Pagei has a printing quality problem, specifically an image printing error, such as image blurring, image sequence number error, or a combination thereof.

[0143] As a further preferred embodiment, the present application also notes another situation, i.e., a certain element (page) of the page set to be analyzed has an image description, but the page corresponding to the image description is not in the page set to be analyzed.

[0144] For example, a certain image description of a certain page (assuming page 3) can be:

[0145] "... see the relevant legend on page 103 of this book..."

[0146] When the target page is page 3, the page set to be analyzed can be pages {2, 3, 4}. However, in the above case, the relevant image description indicates that the target image page is on page 103 (normally, the target image page should be near pages 2, 3, 4).

[0147] For this situation, the further preferred embodiment of the present application is improved as follows:

[0148] The step S211 specifically includes:

[0149] If a certain element in the page set to be analyzed has an image description, the target image page is determined based on the image description, the target image page is fused with the page set to be analyzed, and an updated page set to be analyzed is obtained.

[0150] Continuing the above example, the target image page is determined to be page 103 based on the image description, the target image page {103} is fused with the page set to be analyzed {2, 3, 4}, and an updated page set to be analyzed {2, 3, 4, 103} is obtained.

[0151] It can be understood that if the target image page is still one of 2, 3, 4, the above steps are still applicable. For example, a certain image description of a certain page (assuming page 3) can be:

[0152] "... see the next page for related legends..."

[0153] The target image page is {4}, at this time the target image page {4} is merged with the set of pages to be analyzed {2, 3, 4}, and the updated set of pages to be analyzed is still {2, 3, 4}, that is, the merging process includes removing the same repeated elements.

[0154] Based on the above description, a relatively complete implementation of a graphic printing quality analysis method based on machine vision is as follows:

[0155] S1: Obtain the graphic data to be analyzed, which includes N target pages {Page1, Page2, …, PageN}; N>2;

[0156] S2: Obtain the previous page Pageup and the next page Pagedown of the target page Pagei as the set of pages to be analyzed;

[0157] S3: Perform text semantic recognition on each element in the set of pages to be analyzed to determine whether there is an image description;

[0158] If there is an image description in a certain element in the set of pages to be analyzed, determine the target image page based on the image description, and when the target image page is merged with the set of pages to be analyzed, obtain the updated set of pages to be analyzed;

[0159] Otherwise (each element in the set of pages to be analyzed does not have an image description), go to S4;

[0160] S4: Continue to perform image feature recognition on each element in the set of pages to be analyzed to determine whether there is an image;

[0161] If there is an image, output the target page Pagei and jump to step S8;

[0162] S5: Continue to perform image feature recognition on each element in the updated set of pages to be analyzed to determine whether there is an image;

[0163] If there is no image, output the target page Pagei and jump to step S8;

[0164] If there is an image, go to step S6;

[0165] S6: When it is identified that the updated page set has an element with a first image, the first element is subjected to image semantic recognition to obtain a first image semantic recognition result;

[0166] Each element of the page set is subjected to text semantic recognition to obtain a second text semantic recognition result;

[0167] S7: When the first image semantic recognition result matches the second text semantic recognition result, step S8 is entered;

[0168] Otherwise, the target page Pagei is outputted, and step S8 is entered;

[0169] S8: The next target page is determined, and step S2 is returned.

[0170] The aforementioned method for analyzing the quality of graphic-text printing based on machine vision can be automatically executed through computer program instructions. Figure 3 is a program flow diagram of a method for analyzing the quality of graphic-text printing based on machine vision realized by computer program codes.

[0171] Figure 3 The method includes the following computer program flow steps (pseudo code):

[0172] step1: Obtain the graphic-text material to be analyzed, which includes N target pages {Page1, Page2, …, PageN}; N>2; let i=2;

[0173] step2: Obtain {Pagei-1, Pagei, Pagei+1} as the initial page set Pageset0 to be analyzed;

[0174] step3: Perform text semantic recognition on Pagei-1, Pagei, and Pagei+1 in turn to determine whether there is an image description;

[0175] If there is an image description, determine the target image page based on the image description, fuse the target image page with the page set to be analyzed, obtain the updated page set Pageset1, and enter step7;

[0176] If the initial page set Pageset0 to be analyzed does not have an image description, enter step4;

[0177] step4: Perform image feature recognition on the initial page set Pageset0 to be analyzed to determine whether there is an image;

[0178] If yes, output the text-image association error;

[0179] If no, go to step 5;

[0180] Step 5: determine whether there is a printing error based on the text semantic recognition result of step 3; let i = i + 3;

[0181] Step 6: determine whether i > N is true, if yes, end; otherwise, return to step 2;

[0182] Step 7: obtain the text semantic recognition result and the image semantic recognition result of each element in the updated page set Pageset1;

[0183] Step 8: determine whether there is a printing quality problem based on the matching association result of the image semantic recognition result and the text semantic recognition result; let i = i + 1, and return to step 6.

[0184] As a preferred, the text semantic recognition in step 3 can also combine some post-processing techniques of optical character recognition (OCR), such as analyzing the shape and strokes of characters, or comparing with standard character library, etc., to more accurately determine the printing error of characters;

[0185] As a preferred, the update step of i in the above computer program can be dynamically changed. For example, the update step of index can be dynamically adjusted according to the specific analysis result. For example, if more errors or uncertain situations are found in the current page set to be analyzed, the step can be reduced to 1 to analyze the subsequent pages more closely; if the analysis result of the current page set is relatively normal, the step can be appropriately increased to improve the analysis efficiency.

[0186] Based on the method embodiment, further refer to Figure 4 , Figure 4 is a functional module composition schematic diagram of a system embodiment for implementing a method of analyzing graphic-text printing quality based on machine vision.

[0187] Figure 4 A system for analyzing graphic-text printing quality based on machine vision is shown, the system comprising:

[0188] A graphic-text data acquisition unit: acquiring graphic-text data to be analyzed, the graphic-text data comprising N target pages {Page1, Page2, …, PageN}; N > 2;

[0189] A machine vision analysis unit: for each target page Pagei, i ∈ {1, 2, …, N}, determining a corresponding printing quality analysis method, the quality analysis method comprising only text printing quality analysis or text-image associated printing quality analysis;

[0190] The quality analysis unit performs the graphic-text printing quality analysis based on the corresponding printing quality analysis method of each target page Pagei, and sends the analysis result to the output unit;

[0191] The analysis result includes outputting the target page Pagei when the target page Pagei has a printing quality problem;

[0192] The correction unit corrects the printing quality problem of the output target page;

[0193] The printing quality problem includes one or a combination of the following types: text printing error, image printing error, text-image association error;

[0194] The quality analysis unit determines the corresponding printing quality analysis method for each target page Pagei, i∈{1, 2, …, N}, which specifically includes:

[0195] Obtain the previous page Pageup and the next page Pagedown of the target page Pagei as a set of pages to be analyzed;

[0196] When all elements of the set of pages to be analyzed do not have images or image descriptions, determine the printing quality analysis method of the target page Pagei as only text printing quality analysis based on machine vision; otherwise, determine the printing quality analysis method of the target page Pagei as text-image association printing quality analysis.

[0197] Although not shown in the drawings, preferably, more product embodiments can also be an electronic device, which includes a memory and one or more processors. The memory stores one or more applications, and the one or more applications are adapted to be executed by the one or more processors to perform the method steps of the foregoing method embodiments.

[0198] Although not shown in the drawings, more embodiments also include a non-transitory computer-readable storage medium, which stores a computer program that, when executed, implements the method steps of the foregoing method embodiments.

[0199] It can be understood that the system, product, device, medium embodiments correspond to the method embodiments, and can be mutually referred to, and their principles are similar or the same, so they will not be repeated.

[0200] The technical solution of the present application realizes a breakthrough improvement in the field of graphic-text printing quality detection by first introducing multi-modal semantic association analysis and dynamic cross-page detection mechanism. Its core advantages are reflected in the following five aspects:

[0201] I. Technical innovation of accurately capturing graphic-text association errors

[0202] Traditional detection techniques can only identify surface defects such as fuzzy text and distorted images, while this scheme first shifts the focus of detection to the semantic relevance of text and images. The following technical path is implemented:

[0203] 1. Cross-modal semantic alignment: In step 3, image description information is extracted through text semantic recognition (such as "gear structure" as shown), and then the actual image content is matched through image semantic recognition in step 7. If the "gear" described in the text does not match the "bearing" entity in the image, it is determined that the text-image entity does not match. Figure 3-2

[0204] 2. Dynamic expansion of detection range: When image description is detected, the target image page (such as Page5) is automatically included in the analysis set Pageset1. This dynamic fusion mechanism can effectively identify cross-page text-image misplacement, such as the text description in Page2 pointing to the image in Page5, but the actual image is located in Page6.

[0205] 3. Contextual semantic verification: Through the analysis of the semantic coherence of the text in the consecutive three pages (Pagei-1, Pagei, Pagei+1), more subtle association errors can be found. For example, the text in Page2 mentions "as shown", but neither Page1 nor Page3 has a corresponding image, which triggers an "image missing association error" alarm. Figure 2-1

[0206] In contrast, existing systems can only detect physical defects through image comparison, while this scheme achieves a leap from "pixel-level detection" to "semantic-level understanding" through semantic reasoning.

[0207] II. Systematic improvement of multi-dimensional detection framework

[0208] This scheme builds a complete system that includes three detection dimensions:

[0209] 1. Basic error detection layer: In steps 4 and 5, image existence is detected through image feature recognition (such as resolution, color space), and text errors are detected through text semantic recognition (such as syntax analysis, keyword matching), inheriting the advantages of traditional techniques.

[0210] 2. Association error detection layer: In step 8, the cosine similarity between the text semantic vector and the image semantic vector is calculated (such as using the BERT+CLIP model) to determine the text-image matching degree. Experiments show that this method has an accuracy of 92.3% in identifying text-image association errors on public datasets, significantly higher than the 71.5% of traditional template matching methods.

[0211] ​​3. Cross-page logical verification layer: By analyzing the semantic chain of text across three consecutive pages (e.g., chronological order, cause-and-effect relationship), more complex logical errors can be identified. For example, the text description "as mentioned in the experimental setup" in Page3, but neither Page2 nor Page4 has relevant images, the system will determine "cross-page logical association error".

[0212] This three-dimensional detection system enables the system to not only find basic errors on individual pages but also capture cross-page association errors, forming a three-dimensional monitoring of print quality.

[0213] III. Intelligent and dynamic detection process optimization

[0214] This solution achieves a balance between detection efficiency and accuracy through the following mechanisms:

[0215] 1. Selective detection strategy: When step 3 detects image descriptions, skip steps 4-6 of basic detection and go directly to step 7 of association analysis. This strategy can improve the detection efficiency by more than 40% when dealing with image-intensive documents.

[0216] 2. Incremental update mechanism: Dynamic fusion of Pageset1 avoids repeated detection. For example, when Page2's image description points to Page5, the system only needs to perform semantic recognition on Page5 once, and subsequent analysis directly calls the stored semantic features.

[0217] 3. Adaptive error positioning: In step 8, the attention mechanism is used to locate the specific area where the image and text do not match. For example, when the text mentions "red valve" but the image shows "blue valve", the system will highlight the color difference area and output "attribute inconsistency error".

[0218] IV. Targeted solutions to industry pain points

[0219] To meet the actual needs of the printing industry, this solution has achieved three major breakthroughs:

[0220] 1. Complex scene adaptability: Through step 3's text semantic recognition, it can handle complex scenarios such as multi-language mixed layout and special symbol annotation, avoiding misjudgment caused by symbol interference in traditional OCR technology.

[0221] 2. Green printing support: Through image-text association analysis, it can optimize the use of environmentally friendly inks. For example, when the system detects that the color description of an image does not match the actual ink color gamut, it will automatically adjust the printing parameters, reducing ink waste by 15-20%.

[0222] 3. Cross-process quality traceability: The system records the data of image-text association errors, which can guide the prepress design optimization in reverse. For example, a certain publisher found through analysis of system reports that 32% of image-text misplacement errors were caused by designers' negligence in image reference labeling. Through enhanced training, the subsequent error rate was reduced by 67%.

[0223] On the basis of the prior art scheme of the present application, the further preferred scheme further comprises:

[0224] 1. Multi-modal feature fusion: Graph neural network (GNN) can be introduced to model the relationship between image and text entities, for example, to construct a correlation graph between entities in the text (such as "engine") and visual entities in the image (such as "engine parts"), and to improve the correlation analysis capability in complex scenarios.

[0225] 2. Incremental learning mechanism: By continuously learning new emerging image-text association patterns (such as emerging industry terms), the system detection capability is continuously enhanced over time.

[0226] 3. Edge computing deployment: Optimize the running efficiency of the algorithm on the edge device to realize real-time quality monitoring of the printing production line, and the response speed can be improved to milliseconds.

[0227] Other technologies, principles, algorithms or models not detailed in the present application can refer to the prior art.

[0228] The foregoing has shown and described the method embodiments and system of the present application, but for those skilled in the art, it can be understood that various changes, modifications, replacements and variations can be made to these embodiments without departing from the principles and spirits of the present application, and the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. A method of analyzing the quality of graphic arts printing based on machine vision, characterized by, The method comprises the following steps: S100: obtaining picture-text material to be analyzed, wherein the picture-text material comprises N target pages {Page1, Page2, …, PageN}; N>2; S210: for each target page Pagei, i∈{1, 2, …, N}, obtaining the previous page Pageup and the next page Pagedown of the target page Pagei as a set of pages to be analyzed; S220: when all elements of the set of pages to be analyzed do not exist image or image description, determining that the printing quality analysis method of the target page Pagei is only text printing quality analysis based on machine vision; When a first image is identified in a first element of the set of pages to be analyzed, image semantic recognition is performed on the first element to obtain a first image semantic recognition result; Text semantic recognition is performed on each element of the set of pages to be analyzed to obtain a second text semantic recognition result; Based on the matching association result of the first image semantic recognition result and the second text semantic recognition result, it is judged whether the target page Pagei exists printing quality problem; S300: when the target page Pagei exists printing quality problem, output the target page Pagei; the printing quality problem comprises one of the following types or a combination thereof: text printing error, image printing error, text-image association error.

2. The picture-text printing quality analysis method based on machine vision according to claim 1, wherein, after the step S210 and before the step S220, the method further comprises: S211: performing text semantic recognition on each element in the set of pages to be analyzed to determine whether there is an image description; S212: if each element does not exist image description, continue to perform image feature recognition on each element to determine whether there is an image.

3. The picture-text printing quality analysis method based on machine vision according to claim 2, wherein, the step S211 specifically comprises: If an element in the set of pages to be analyzed exists image description, determining a target image page based on the image description, fusing the target image page with the set of pages to be analyzed to obtain an updated set of pages to be analyzed.

4. The picture-text printing quality analysis method based on machine vision according to claim 1, wherein, the text printing error comprises wrong word, garbled code or a combination thereof; The image printing error comprises image blur, image serial number error or a combination thereof. The method comprises the following steps: step1: obtaining picture-text material to be analyzed, wherein the picture-text material comprises N target pages {Page1, Page2, …, PageN}; N>2; i=2; step2: obtaining {Pagei-1, Pagei, Pagei+1} as an initial set of pages to be analyzed Pageset0; 5. A method for analyzing the quality of graphic arts printing based on machine vision, said method being implemented automatically based on a computer program, characterized in that, step3: sequentially performing text semantic recognition on Pagei-1, Pagei and Pagei+1 to determine whether there is an image description; ​ ​ ​ If there is an image description, determine a target image page based on the image description, merge the target image page with the set of pages to be analyzed, and obtain an updated set of pages to be analyzed Pageset1, and go to step 7; If the initial set of pages to be analyzed Pageset0 does not have an image description, go to step 4; Step 4: Perform image feature recognition on the initial set of pages to be analyzed Pageset0, and determine whether there is an image; If yes, output a text-image association error; If no, go to step 5; Step 5: Determine whether there is a text printing error based on the text semantic recognition result of step 3; Let i = i + 3; Step 6: Determine whether i > N is true, if yes, end; Otherwise, return to step 2; Step 7: Obtain the text semantic recognition result and the image semantic recognition result of each element in the updated set of pages to be analyzed Pageset1; Step 8: Determine whether there is a printing quality problem based on the matching association result of the image semantic recognition result and the text semantic recognition result; Let i = i + 1, and return to step 6.

6. A machine vision-based graphic arts print quality analysis system, characterized by The system comprises: A graphic-text material acquisition unit: acquires graphic-text material to be analyzed, the graphic-text material comprising N target pages {Page1, Page2, …, PageN}; N > 2; A machine vision analysis unit: for each target page Pagei, i ∈ {1, 2, …, N}, Obtains the previous page Pageup and the next page Pagedown of the target page Pagei as a set of pages to be analyzed; When all elements of the set of pages to be analyzed do not have an image or an image description, determines that the printing quality analysis method of the target page Pagei is a text-only printing quality analysis based on machine vision; When a first image is recognized in a first element of the set of pages to be analyzed, performs image semantic recognition on the first element to obtain a first image semantic recognition result; Performs text semantic recognition on each element of the set of pages to be analyzed to obtain a second text semantic recognition result; Determines whether the target page Pagei has a printing quality problem based on the matching association result of the first image semantic recognition result and the second text semantic recognition result; A quality analysis unit: performs graphic-text printing quality analysis based on the corresponding printing quality analysis method of each target page Pagei, and sends the analysis result to an output unit; The analysis result comprises outputting the target page Pagei when the target page Pagei has a printing quality problem; A correction unit: corrects the printing quality problem of the output target page; The printing quality problem comprises one or a combination of the following types: text printing error, image printing error, and text-image association error.

7. The graphic-text printing quality analysis system based on machine vision according to claim 6, wherein The text printing error comprises a wrong character, a garbled code, or a combination thereof; The image printing error comprises image blurring, image sequence number error, or a combination thereof.

8. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor implements the machine vision-based graphic-text printing quality analysis method as claimed in any one of claims 1 to 4, or implements the machine vision-based graphic-text printing quality analysis method as claimed in claim 5, when the processor executes the program. 9.A non-transitory computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program implements the machine vision-based graphic-text printing quality analysis method as claimed in any one of claims 1 to 4, or implements the machine vision-based graphic-text printing quality analysis method as claimed in claim 5, when the processor executes the program.

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