File header-containing file sleeve printing method and device, computer equipment and storage medium

Abstract

The invention relates to a file-header-containing file sleeve printing method and device, computer equipment and a storage medium. The method comprises the steps of obtaining a to-be-printed file in a picture format; converting the to-be-copied file into a target document format, and extracting structured document information of the to-be-copied file through an analysis model of the target document format; extracting characters in the to-be-printed file through the picture recognition model to obtain a searchable text; retrieving the searchable text based on the header text, determining the position of the header and deleting the header text content in the searchable text to obtain a header-removed text; and obtaining visual format features of the file to be printed through the document analysis model, and reconstructing the header-removed text based on the visual format features to obtain the header-removed file as a printed document. Through the method provided by the invention, the text header can be removed, the visual features of the text can be kept, the text can be directly printed on the pre-printed text header paper, manual operation is not needed, and the working efficiency and the accuracy rate are improved.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular to a method, apparatus, computer device, and storage medium for printing header-containing documents. Background Technology

[0002] Some documents include a header and a body, with the header located in the upper center. In actual office work, printing documents with headers presents special requirements: the first page uses pre-printed header paper, while subsequent content usually only needs to be printed in normal black and white.

[0003] The traditional method involves using pre-printed header paper, manually pasting white paper to cover the header area before printing, and then removing the white paper after printing. This method is inefficient, increases workload, and is prone to causing paper jams in the printer. Summary of the Invention

[0004] Therefore, it is necessary to provide a method, apparatus, computer equipment, and readable storage medium for printing header documents without manual intervention, addressing the aforementioned technical problems.

[0005] Firstly, this application provides a method for printing a header-containing document, the method comprising: Obtain the image-formatted document to be printed; the document to be printed includes a header and the main text in the middle. The document to be printed is converted into a target document format, and the document to be printed is parsed using the parsing model of the target document format to extract the structured document information of the document to be printed. The text in the document to be printed is extracted using an image recognition model to obtain searchable text; the searchable text includes header text and body text. The searchable text is searched based on the header text, the location of the header is determined, and the header text content in the searchable text is deleted to obtain the header-free text. The structured document information is extracted using a deep learning-based document parsing model to obtain the visual format features of the document to be printed. Based on these visual format features, the header-removed text is reconstructed to obtain the header-removed document as the printable document.

[0006] Furthermore, the structured document information includes graphics rendering instructions; before extracting the text from the document to be printed using an image recognition model to obtain searchable text, the method further includes: The graphics rendering instructions are extracted using a content-aware adaptive rendering model, and the file to be printed is rendered based on the graphics rendering instructions.

[0007] Furthermore, the rendering process of the file to be printed using a content-aware adaptive rendering model includes: The file to be printed is input into a content-aware adaptive rendering model; The adaptive rendering model splits the file to be printed into multiple elements, identifies the element category of each element, and calls the corresponding optimization model for rendering according to the element category; the element categories include text and graphics.

[0008] Furthermore, the step of calling the corresponding optimized model for rendering according to the element category includes: When the element category is a graphic, an anti-aliasing algorithm model is used to render the element; When the element category is text, a resolution optimization model is used to render the element.

[0009] Furthermore, after rendering the file to be printed using a content-aware adaptive rendering model, the method further includes: Noise is removed from the file to be printed using a denoising model; The resolution of the file to be printed is processed by an image super-resolution model so that the resolution of the denoised file reaches a preset threshold.

[0010] Furthermore, the structured document information includes the page topology, the font features of the main text, and the text content flow; the page topology includes the page size, margins, and content area; the font features include the font name, size, color, bold information, and italic information; the text content flow includes the text string itself, as well as the precise coordinate position of each character or text block on the page.

[0011] Secondly, this application also provides a document printing device with a header, the device comprising: The acquisition module is used to acquire a file to be printed in image format; the file to be printed includes a header and the main text in the middle. The extraction module is used to convert the document to be printed into a target document format, and to perform document parsing on the document to be printed using the parsing model of the target document format to extract the structured document information of the document to be printed. The recognition module is used to extract text from the document to be printed using an image recognition model to obtain searchable text; the searchable text includes header text and body text. The header removal module is used to search the searchable text based on the header text, determine the location of the header, and delete the header text content in the searchable text to obtain the header-removed text. The reconstruction module is used to extract the structured document information through a deep learning document parsing model, obtain the visual format features of the document to be printed, and reconstruct the header-removed text based on the visual format features to obtain the header-removed document as the printable document.

[0012] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method described in any of the above claims.

[0013] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in any of the preceding claims.

[0014] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the method described in any of the above claims.

[0015] In the aforementioned method, apparatus, computer equipment, and storage medium for printing documents with headers, a document to be printed (containing both header and body text) in image format is acquired. This document is then converted to a target document format. The structured document information of the document is extracted using a parsing model. Text is extracted from the document to obtain searchable text. The header text content is deleted from the searchable text to obtain header-free text. The structured document information is extracted to obtain the visual format features of the document. Based on these visual format features, the header-free text is reconstructed to obtain the header-free document for printing. This process removes the header while preserving the visual features of the body text, allowing direct printing onto pre-printed header paper without manual intervention, thus improving efficiency and accuracy. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments or related technologies of this application, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a diagram illustrating the application environment of a header file overlay printing method in one embodiment. Figure 2 This is a flowchart illustrating a method for printing header files in one embodiment; Figure 3 This is a structural block diagram of a header document printing device in one embodiment; Figure 4This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0019] The header-containing document printing method provided in this application can be applied to, for example... Figure 1 In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be integrated onto server 104 or located on the cloud or other network servers. Terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, smart in-vehicle devices, etc. Portable wearable devices can include smartwatches, smart bracelets, head-mounted devices, etc. Server 104 can be implemented using a standalone server or a server cluster consisting of multiple servers.

[0020] In some scenarios, there are special requirements for printing documents with headers: the first page uses pre-printed header paper, while the subsequent content usually only needs to be printed in normal black and white. In order to avoid the header on the document from conflicting with the pre-printed header, the traditional solution is to use pre-printed header paper, manually paste white paper to cover the header area before printing, and then remove the white paper after printing.

[0021] In one exemplary embodiment, such as Figure 2 As shown, a method for printing header files is provided, which can be applied to... Figure 1 Taking server 104 as an example, the explanation includes the following steps 202 to 210. Wherein: Step 202: Obtain the image-formatted document to be printed; the document to be printed includes a header and the body text in the middle.

[0022] For example, the document to be printed includes a header section and the body text in the middle. Optionally, the header section may contain basic information, including specific formatting elements such as the title, document number, and official seal. Optionally, the header may be a title, company or organization name, date, address, or other important opening content, such as the red header in formal official documents. Optionally, the body text is the main content of the document, containing detailed information, specific instructions, terms, agreements, or any other main content that needs to be conveyed.

[0023] Step 204: Convert the document to be printed into the target document format, and use the parsing model of the target document format to parse the document to be printed and extract the structured document information of the document to be printed.

[0024] For example, the target document format may be a format that supports text and layout structure parsing, such as Word, PDF, or Excel. Further, the target document format may be PDF.

[0025] For example, a parsing model of the target document format is used to perform in-depth document parsing on the document to be printed. The parsing model extracts the document's layout structure, font style, paragraph spacing, table structure, and other content to form structured document information.

[0026] Step 206: Extract the text from the document to be printed using an image recognition model to obtain searchable text; the searchable text includes header text and body text.

[0027] For example, the image recognition model can identify the format of a document to be printed (image format), extract the text content, and obtain searchable text. The identified searchable text content includes header text and body text; at this point, the searchable text consists only of text and does not have a specific format.

[0028] For example, the image recognition model can employ an OCR (Optical Character Recognition) algorithm.

[0029] Step 208: Search the searchable text based on the header text, determine the location of the header, and delete the header text content in the searchable text to obtain the header-free text.

[0030] For example, content retrieval is performed within the searchable text based on the extracted header text. The header text is compared with other text within the searchable text to confirm its exact location within the entire document. Optionally, a text matching algorithm can be used.

[0031] For example, after deleting the header text, the resulting text without the header will be a simplified version of the document, with unnecessary opening information removed, and the remaining part is the main text content.

[0032] Step 210: Extract structured document information using a deep learning document parsing model to obtain the visual format features of the document to be printed. Reconstruct the header-free text based on the visual format features to obtain the header-free document as the printable document.

[0033] For example, deep learning models can be convolutional neural networks (CNNs) or transformer models, which can effectively identify and analyze the visual structure and typographic features in a document, such as text arrangement, font style, paragraph spacing, line spacing, heading levels, and text alignment. These visual formatting features are important elements that determine the appearance of a document, including font, color, bold, italics, line spacing, and margins.

[0034] By analyzing the header-removed text using a deep learning-based document parsing model, the visual formatting features of each part of the document are extracted. For example, the specific layout of formatting elements such as paragraphs, headings, lists, and tables can be identified.

[0035] Optionally, visual formatting features include text styles, paragraph and line layouts, heading and body text hierarchy, tables, and images. For example, text styles include font size, color, bold, italics, etc. Paragraph and line layouts include paragraph indentation, alignment, line spacing, and margins. Heading and body text hierarchy identifies different levels of headings in the document through features such as heading size and bolding. Tables and images are identified, and their layout and position within the document are analyzed.

[0036] For example, based on the extracted visual format features, the header-removed text can be reconstructed, that is, the text content after header removal is reformatted according to the visual format of the original document, so that the header-removed text can present the same visual effect as the original document while maintaining the integrity of the content. The reconstructed header-removed file will fully conform to the predetermined printing document standard and can be directly printed on pre-printed header paper.

[0037] In the above-described method for printing documents with headers, a document to be printed (containing both header and body text) in image format is obtained. This document is then converted to a target document format, and its structured document information is extracted using a parsing model. Text is extracted from the document to obtain searchable text. The header text is then removed from the searchable text to obtain header-free text. The structured document information is further extracted to obtain the visual format features of the document. Based on these visual format features, the header-free text is reconstructed to obtain the header-free document for printing. This process removes the header while preserving the visual features of the body text, allowing direct printing onto pre-printed header paper without manual intervention, thus improving efficiency and accuracy.

[0038] Furthermore, the structured document information also includes graphics rendering instructions; before extracting the text from the document to be printed using an image recognition model to obtain searchable text, the method also includes: extracting graphics rendering instructions using a content-aware adaptive rendering model, and rendering the document to be printed based on the graphics rendering instructions.

[0039] For example, structured document information also includes graphics rendering instructions. In the process of processing documents to be printed, the document is not only composed of text, but also includes various graphics, images, background designs, borders, icons, and other visual elements. These graphic elements are part of the document's visual presentation and play an important role in the overall format and effect.

[0040] For example, the rendering process includes adjustments to graphics, text, colors, and other elements to integrate document elements into the overall document. This includes image optimization, graphic overlap handling, and graphics-text coordination. Image optimization involves optimizing the resolution of image elements in the document. Graphic overlap handling adjusts the position, size, and opacity of elements to avoid overlap. Graphic-text coordination involves adjusting attributes such as color, texture, and contrast of graphics to ensure visual harmony between graphic elements and text content.

[0041] Furthermore, a content-aware adaptive rendering model is adopted to render the file to be printed, including: inputting the file to be printed into the content-aware adaptive rendering model; splitting the file to be printed into multiple elements through the adaptive rendering model, identifying the element category of each element, and calling the corresponding optimization model for rendering according to the element category; the element categories include text and graphics.

[0042] For example, the document to be printed may contain various elements such as text, graphics, and images. Inputting the document into a content-aware adaptive rendering model can identify the basic content of the text and graphics, break down the document into multiple elements, and identify the element category of each element. Element categories can be text, graphics, images, tables, lists, etc.

[0043] Optionally, by identifying the category of each element, the rendering model can select the most suitable optimization model for rendering based on the different element types. Different element types require different processing methods.

[0044] Furthermore, rendering according to the element category and calling the corresponding optimization model includes: when the element category is a graphic, an anti-aliasing algorithm model is used to render the element; when the element category is text, a resolution optimization model is used to render the element.

[0045] For example, to ensure that different types of elements can present the best effect during the rendering process, the system selects the most suitable optimization model for processing based on the element's category. Different optimization techniques are used when processing graphic and text elements to meet their respective visual presentation requirements.

[0046] When the element type is graphic, an anti-aliasing algorithm is used for rendering. Since graphic edges often exhibit jagged edges, especially at low resolutions, these jagged edges can severely impact the visual quality. Therefore, when the system recognizes an element as graphic, it invokes an anti-aliasing algorithm for processing. Anti-aliasing is a technique that optimizes the smoothness of graphic edges by weighted blending of pixels at the edges, reducing or eliminating jaggedness and making the graphic appear smoother and more natural.

[0047] When the element type is text, a resolution optimization model is used for rendering. This model uses algorithms to fine-tune the text, automatically adapting to the display requirements of different resolutions. Specifically, it adjusts parameters such as anti-aliasing, edge smoothing, font size, and glyphs.

[0048] Furthermore, after rendering the printout using a content-aware adaptive rendering model, the method also includes: removing noise from the printout using a denoising model; and performing resolution processing on the printout using an image super-resolution model to bring the resolution of the denoised file to a preset threshold.

[0049] For example, the denoising model, based on deep learning or traditional image processing algorithms, can intelligently identify and effectively remove noise in a file. The denoising process includes filtering out random noise, removing blur, color differences, or irregular brightness variations.

[0050] For example, image super-resolution models are techniques used in deep learning to reconstruct high-resolution images from low-resolution images. By using local and global information, image super-resolution models generate higher-resolution image details, restoring details that may be lost when an image is magnified. Specifically, super-resolution models enhance image sharpness by predicting finer pixel structures.

[0051] Furthermore, the structured document information includes the page topology, the font characteristics of the main text, and the text content flow; the page topology includes the page size, margins, and content areas; the font characteristics include the font name, size, color, bold information, and italic information; the text content flow includes the text string itself, as well as the precise coordinate position of each character or text block on the page.

[0052] For example, page size refers to the length and width of the document to be printed. Margins refer to the distance between the text and the edge of the page. Content area refers to the area where the header and body text are located; it can be a range represented by coordinates. Font name refers to a name such as "SimSun" or "KaiTi". Size refers to the font size.

[0053] Text content flow refers to the actual text information in a document and its spatial distribution on the page, including the specific content of the text and the precise location of text blocks. The text string itself: The text string refers to the actual text content in the document and is the most basic element of the document. Each character or text block has its precise coordinate position on the page, including the position of the top-left corner (such as the X and Y axis coordinates), and its relative position to other elements (such as images, tables, margins, etc.).

[0054] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0055] Based on the same inventive concept, this application also provides a header-containing document printing apparatus for implementing the above-described header-containing document printing method. The solution provided by this apparatus is similar to the solution described in the above method; therefore, the specific limitations in one or more header-containing document printing apparatus embodiments provided below can be found in the limitations of the header-containing document printing method described above, and will not be repeated here.

[0056] In one exemplary embodiment, such as Figure 3 As shown, a document printing device 300 with header information is provided, including: an acquisition module 302, an extraction module 304, an identification module 306, a header removal module 308, and a reconstruction module 310. Wherein: The acquisition module 302 is used to acquire the image format file to be printed; the file to be printed includes a header and the main text in the middle; Extraction module 304 is used to convert the file to be printed into the target document format, and to perform document parsing on the file to be printed using the parsing model of the target document format to extract the structured document information of the file to be printed. The recognition module 306 is used to extract text from the document to be printed using an image recognition model to obtain searchable text; the searchable text includes header text and body text. The header removal module 308 is used to search the searchable text based on the header text, determine the location of the header, and delete the header text content in the searchable text to obtain the header-removed text. The reconstruction module 310 is used to extract structured document information through a deep learning document parsing model, obtain the visual format features of the document to be printed, and reconstruct the header-removed text based on the visual format features to obtain the header-removed document as the print document.

[0057] Each module in the aforementioned header document printing device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a computer device in hardware form or independent of it, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0058] In one exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 4 As shown, this computer device includes a processor, memory, input / output interfaces (I / O), and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores file data. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network connection. When executed by the processor, the computer program implements a header file printing method.

[0059] Those skilled in the art will understand that Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0060] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0061] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps in the above method embodiments.

[0062] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0063] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0064] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0065] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for cover printing a header file, characterized by, The method comprises: acquiring a to-be-printed file in a picture format; the to-be-printed file comprises a header located at a head and a body located in a middle part; converting the to-be-printed file into a target document format, performing document analysis on the to-be-printed file through a target document format analysis model, and extracting structured document information of the to-be-printed file; extracting text in the to-be-printed file through a picture recognition model to obtain searchable text; the searchable text comprises header text and body text; searching the searchable text based on the header text, determining a position of the header, and deleting header text content in the searchable text to obtain header-removed text; extracting the structured document information through a deep learning document analysis model, acquiring visual format features of the to-be-printed file, reconstructing the header-removed text based on the visual format features, and obtaining a header-removed file as a print document.

2. The method of claim 1, wherein, The structured document information comprises graphic rendering instructions; before the step of extracting text in the to-be-printed file through a picture recognition model to obtain searchable text, the method further comprises: extracting the graphic rendering instructions through a content-aware adaptive rendering model, and performing rendering processing on the to-be-printed file based on the graphic rendering instructions.

3. The method of claim 2, wherein, The step of performing rendering processing on the to-be-printed file through the content-aware adaptive rendering model comprises: inputting the to-be-printed file into the content-aware adaptive rendering model; splitting the to-be-printed file into multiple elements through the adaptive rendering model, identifying element categories of the elements, and calling corresponding optimization models for rendering according to the element categories; the element categories comprise text and graphics.

4. The method of claim 3, wherein, The step of calling corresponding optimization models for rendering according to the element categories comprises: in a case where the element category is graphics, performing rendering on the element through an anti-aliasing algorithm model; in a case where the element category is text, performing rendering on the element through a resolution optimization model.

5. The method of claim 2, wherein, After the step of performing rendering processing on the to-be-printed file through the content-aware adaptive rendering model, the method further comprises: removing noise in the to-be-printed file through a denoising model; performing resolution processing on the to-be-printed file through an image super-resolution model, so that a resolution of the denoised file reaches a preset threshold.

6. The method of claim 1, wherein, The structured document information comprises page topology, font features of the body, and text content flow; the page topology comprises page size, margin, and content area; the font features comprise font name, size, color, bold information, and italic information; and the text content flow comprises a text string itself and accurate coordinate positions of each character or text block on a page.

7. A header file containing batch printing device, characterized by, The device comprises: an acquisition module configured to acquire a to-be-printed file in a picture format; the to-be-printed file comprises a header located at a head and a body located in a middle part; an extraction module configured to convert the to-be-printed file into a target document format, perform document analysis on the to-be-printed file through a target document format analysis model, and extract structured document information of the to-be-printed file; The identification module is configured to extract text in the to-be-printed file by using a picture identification model to obtain searchable text, wherein the searchable text comprises header text and body text. The header removal module is configured to search the searchable text based on the header text, determine a position of the header, and remove header text content in the searchable text to obtain header-removed text. The reconstruction module is configured to extract the structured document information by using a deep learning document parsing model, acquire visual format features of the to-be-printed file, reconstruct the header-removed text based on the visual format features, and obtain a header-removed file as a print document.

8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that, The processor executes the computer program to implement the steps of the method in any one of claims 1 to 6.

9. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the method in any one of claims 1 to 6.

10. A computer program product comprising a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the method in any one of claims 1 to 6.

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