System and method for identifying printed product of color laser printer

Abstract

The invention relates to a color laser printer printed product identification system and method, and the system comprises a scanning module which is used for scanning a printed product printed by a printer to form a first image, and the printed product is provided with a special mark; the image processing module is used for preprocessing the first image to obtain a second image; the transcoding module is connected to the image processing module or the scanning module, and is used for identifying a special mark in the first image or the second image to obtain a single mark matrix representing printing information; and the decoding module is used for interpreting the single mark matrix to obtain printing information corresponding to the printed product. The scanning module captures special marks on the surface of a printed product through a high-resolution imaging technology, and complete extraction of tiny marks is ensured. The image processing module performs preprocessing such as noise reduction, contrast enhancement and geometric correction on an original image, so that the definition and identifiability of a mark are remarkably improved, and interference factors such as paper wrinkles and stains are effectively handled.

Description

Technical Field

[0001] The present application relates to the field of identification technology, and in particular to a system and method for identifying printed products of a color laser printer. Background Art

[0002] In today's society, various documents, such as applications and certificates, are circulated frequently, in both traditional paper and digital formats. Compared to the limitations of electronic documents, which rely on electronic devices for viewing, printed documents remain widely used due to their low cost, high visibility, and ease of portability and distribution.

[0003] Laser printing technology uses a laser beam to rapidly "project" digitized images or documents onto the surface of a photosensitive drum. Its core principle is laser imaging. When the laser beam strikes a specific location on the photosensitive drum, it triggers an electron discharge, which attracts fine toner particles through electrostatic attraction, ultimately creating a clear print. While the widespread adoption and advancement of color laser printing technology has brought convenience, its efficiency and convenience have also posed new social challenges. For example, criminals have exploited this technology to forge paper documents, receipts, and even currency, posing a serious threat to public security. To enhance the security of printed documents, electronic watermarking technology is being used to embed hidden information for counterfeit detection and document management. By superimposing digital information on printed content, this technology supports a variety of applications, including copyright protection and source tracing. Color laser printers automatically embed hidden identification marks during output to support document authentication and traceability.

[0004] However, printed documents embedded with electronic watermarks face numerous challenges in the reading process. Because these marks are often small and complex, existing recognition equipment suffers from slow speed and inaccuracy. The absence or omission of a mark can directly lead to misjudgment of the document information, compromising anti-counterfeiting effectiveness. Watermark information is typically extracted with the aid of a scanner or other device, or verified by comparing it with the original document content. However, scanners may fail to read due to temporary malfunctions, wrinkles on the paper, or stains. Insufficient toner in a printer can also cause uneven print density, making the watermark information difficult to identify. Furthermore, even in copiers equipped with electronic watermark reading capabilities, the aforementioned issues can still lead to information extraction failures. Summary of the Invention

[0005] Based on this, it is necessary to provide a color laser printer printout recognition system and method with excellent stability and high recognition efficiency to address the above technical problems.

[0006] In a first aspect, the present application provides a color laser printer printout recognition system, the color laser printer printout recognition system comprising:

[0007] A scanning module, configured to scan a printed product printed by a printer to form a first image, wherein the printed product has a special mark;

[0008] an image processing module, connected to the scanning module, and configured to pre-process the first image to obtain a second image;

[0009] a transcoding module, connected to the image processing module or the scanning module, for identifying the special mark in the first image or the second image to obtain a single mark matrix representing printed information; and

[0010] A decoding module is connected to the transcoding module and is used to interpret the single mark matrix to obtain the printing information corresponding to the printed product.

[0011] In one embodiment, the scanning module adopts a spectral scanner, and the scanning imaging wavelength range of the spectral scanner is 320-1200 nm.

[0012] In one embodiment, the image processing module is preset with an image preprocessing algorithm, including at least one of: dual-domain decomposition layer, retinal neural dynamic compression, phase-guided spatial domain reconstruction, and frequency domain-spatial domain joint denoising.

[0013] In one embodiment, the second image is a narrow-band spectral image.

[0014] In one embodiment, the transcoding module includes:

[0015] an identification unit, configured to identify all the special marks of the first image or the second image and obtain an overall mark matrix of the first image;

[0016] The dividing unit is used to extract a single mark matrix representing printing information from the overall mark matrix.

[0017] In one embodiment, the color laser printer printout recognition system further includes a grid module, which is used to divide the single marking matrix into grids such that each grid has a corresponding preset number of marking points.

[0018] In a second aspect, the present application provides a printed matter recognition method, the printed matter recognition method comprising the steps of:

[0019] Scanning a printed product printed by the printer to form a first image, wherein the printed product has a special mark;

[0020] Preprocessing the first image to obtain a second image;

[0021] Recognizing the special mark in the first image or the second image to obtain a single mark matrix representing printed information;

[0022] The single mark matrix is interpreted to obtain the printing information corresponding to the printed product.

[0023] In one embodiment, in the step of scanning the printed product printed by the printer to form the first image, spectral scanning is adopted, and the scanning imaging wavelength range of the spectral scanning is 320-1200 nm.

[0024] In one embodiment, the step of scanning and identifying the special mark in the first image or the second image to obtain a single mark matrix representing printed information further includes:

[0025] identifying all the special marks of the first image or the second image, and obtaining an overall mark matrix of the first image or the second image;

[0026] A single mark matrix representing printing information is extracted from the overall mark matrix.

[0027] In one embodiment, after the step of scanning and identifying the special mark in the first image or the second image to obtain a single mark matrix representing printed information, the step further includes: dividing the single mark matrix into a grid so that each grid has a corresponding preset number of mark points.

[0028] In the aforementioned color laser printer print recognition system, the scanning module uses high-resolution imaging technology to capture special markings on the printed product surface (such as laser-printed covert markings or electronic watermarks), ensuring complete extraction of tiny markings. The image processing module performs pre-processing on the original image, including noise reduction, contrast enhancement, and geometric correction, significantly improving the clarity and legibility of the markings and effectively addressing interference factors such as paper wrinkles and stains. The transcoding module uses algorithms to convert complex image information into a single marking matrix, reducing data redundancy and increasing information density. The decoding module utilizes efficient decoding algorithms (such as error-correcting coding) to restore the complete printed information through matrix logic relationships, even if markings are partially missing or blurred, ensuring accurate and fault-tolerant recognition. The system is optimized for the characteristics of laser-printed markings and electronic watermarks, adapting to varying print quality (such as toner concentration fluctuations) and paper conditions (such as minor soiling). By analyzing unique printed information contained in the special markings (such as timestamps, device numbers, and encryption identifiers), the system can accurately trace the document source and printing device, effectively curbing illegal copying. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0030] Figure 1 This is an example diagram of a printed product in one embodiment of the present application;

[0031] Figure 2 This is a schematic structural diagram of an identification device in one embodiment of the present application;

[0032] Figure 3 This is an example diagram of the first image in one embodiment of the present application.

[0033] Figure 4 This is an example diagram of the second image in one embodiment of the present application.

[0034] Figure 5 This is an example diagram of the division of a single marker matrix in one embodiment of the present application.

[0035] Figure 6 This is another example diagram of the second image in one embodiment of the present application.

[0036] Figure 7 This is another example diagram of the second image in one embodiment of the present application.

[0037] Figure 8 This is a structural diagram of a grid module in one embodiment of the present application.

[0038] Figure 9 This is a structural diagram of an identification system in one embodiment of the present application. DETAILED DESCRIPTION

[0039] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0040] The color laser printer printout recognition system and method provided in the embodiments of the present application are suitable for processing Figure 1 The printout shown can be used for Figure 2 The identification device 10 shown in FIG. The identification device 10 is briefly introduced below. The identification device 10 includes a box 200 , a multi-spectral loading glass 300 and a multi-spectral imaging device 100 .

[0041] The housing 200 includes an identification chamber 201 for placing documents. A multispectral loading glass 300 is provided in the housing 200, and the multispectral loading glass 300 has a loading surface for placing documents. The multispectral loading glass 300 is made of a light-transmitting material so that the multispectral imaging device 100 can capture images formed by the documents projected onto the multispectral loading glass 300. The multispectral imaging device 100 is movably connected to the housing 200, and the multispectral imaging device 100 is provided on the side of the multispectral loading glass 300 away from the loading surface, that is, the multispectral loading glass 300 is located between the placed documents and the multispectral imaging device 100. The multispectral imaging device 100 is movably connected to the housing 200, and the multispectral imaging device 100 can move relative to the housing 200, thereby improving the accuracy and comprehensiveness of the collection. The imaging wavelength range of the multispectral imaging device 100 is the same as the light-transmitting wavelength range of the multispectral carrier glass 300. By setting a light-transmitting multispectral carrier glass 300, and matching the light-transmitting wavelength of the multispectral carrier glass 300 with the imaging wavelength of the multispectral imaging device 100, it can be ensured that all the pixels required on the document can pass through the multispectral carrier glass 300 and can be collected by the multispectral imaging device 100. There is no need for multiple collections or replacement of different multispectral carrier glasses 300 for identification, which improves the identification speed and efficiency, improves the identification accuracy, and thereby improves the problem of inaccurate document information caused by missing or omission of marks.

[0042] The box 200 can be either open or closed. In some embodiments, the box 200 includes a main body 210 and a cover 220 movably connected to the main body 210. The main body 210 and the cover 220 enclose an identification chamber 201. The cover 220 is provided with a pressing portion 221 protruding toward the identification chamber 201. The main body 210 and the cover 220 are pivotally connected via a rotating shaft, and the cover 220 can selectively seal the identification chamber 201. When the cover 220 is closed, the protruding pressing portion 221 exerts a force toward the document, thereby increasing the fit between the document and the loading surface.

[0043] The pressing portion 221 is provided with a covering (not shown). This covering is made of an elastic or deformable material, providing a good pressing effect and protecting documents. A sealing portion 222 is located between the pressing portion 221 and the edge of the cover 220. The sealing portion 222 connects the protruding pressing portion 221 and the edge of the cover 220 with a gradually varying depth. When the body 210 and the cover 220 are connected, the sealing portion 222 is sealed to the edge of the body. A sealing ring is provided on the sealing portion 222. The sealing portion 222 and the sealing ring are connected to the body to achieve a sealed seal for the box 200.

[0044] The housing 200 is made of a transparent material, and the cover 220 is made of a light-proof material. The transparent housing 200 allows the user to easily observe and operate the multispectral imaging device 100, while the light-proof cover 220 serves as a backplate for the multispectral imaging device 100 to collect documents and prevents light leakage from affecting the collection efficiency and accuracy of the multispectral imaging device 100.

[0045] The multispectral glass 300 is located in the middle of the housing 200, dividing the identification chamber 201 into a storage area and a collection area. Documents can be placed in the storage area, and the multispectral imaging device 100 is located in the collection area. The multispectral glass 300 comprises a multispectral material. The multispectral glass 300 can be composed of a multispectral material or a translucent material with a multispectral coating attached. Correspondingly, the multispectral imaging device 100 is a multispectral imaging device 100, such as a multispectral camera. The multispectral translucent material can be multispectral translucent glass, silicon, polymer, boron nitride, zinc sulfide, etc.

[0046] The identification device 10 further includes an illuminating element 400, which is movably connected to the housing 200 and is capable of moving relative to the housing 200 in at least a first direction. The housing 200 is provided with a guide member 211 arranged along the first direction. The illuminating element 400 is movably connected to the guide member 211 and moves along a path defined by the guide member 211. Furthermore, the identification device 10 further includes a drive assembly, which is disposed in a placement area of the housing 200 and is configured to drive the illuminating element 400 to reciprocate in the first direction.

[0047] The multispectral imaging device 100 is movably connected to the lighting element 400, enabling movement relative to the lighting element 400 in a second direction. Because the lighting element 400 itself is capable of movement in the first direction, the multispectral imaging device 100 as a whole is capable of movement relative to the housing 200 in at least the first and second directions, enabling the multispectral imaging device 100 to capture substantially all portions of the document. The drive unit also enables reciprocating movement of the multispectral imaging device 100 in the second direction.

[0048] Furthermore, the lighting component 400 includes a bracket 410 and a light-emitting unit 420 provided on the bracket 410. The bracket 410 is movably connected to the guide member 211 in the box body 200. The bracket 410 moves relative to the guide member 211 along a first direction under the action of the driving assembly, driving the light-emitting unit 420 to complete the illumination of the document. The bracket 410 is arranged along a second direction, and the multi-spectral imaging device 100 is movably connected to the bracket 410 and moves along a path defined by the bracket 410 under the action of the driving assembly. The two ends of the bracket 410 are respectively connected to the box body 200. A plurality of light-emitting units 420 are provided on the bracket 410, and the plurality of light-emitting units 420 cover the width or length direction of the document. The above-mentioned first direction is defined as one of the length or width directions of the document, and the second direction is defined as the other of the length or width directions of the document. The first direction and the second direction are perpendicular.

[0049] like Figure 9 As shown, the color laser printer printout recognition system provided in the embodiment of the present application, based on the above-mentioned recognition device 10, can be used to recognize, for example, markings formed by printing on documents. Documents include all printed paper recording materials such as official documents, letters, contracts, bills, etc. Printed documents, in particular, are printed documents with secret codes generated by a color laser printer.

[0050] The recognition system includes a scanning module 11, an image processing module 12, a transcoding module 13, and a decoding module 14. The scanning module 11 is used to scan a printed product from a printer to form a first image, wherein the printed product has a special marking. The image processing module 12 is connected to the scanning module 11 and is used to pre-process the first image to obtain a second image. The transcoding module 13 is connected to the image processing module 12 or the scanning module 11 and is used to identify the special marking in the first image or the second image to obtain a single marking matrix representing the printed information. The decoding module 14 is connected to the transcoding module 13 and is used to interpret the single marking matrix to obtain the printed information corresponding to the printed product.

[0051] In the above-mentioned recognition system, the scanning module 11 uses high-resolution imaging technology to capture special markings on the printed surface (such as laser-printed covert markings or electronic watermarks), ensuring complete extraction of tiny markings. The image processing module 12 performs pre-processing on the original image, including noise reduction, contrast enhancement, and geometric correction, significantly improving the clarity and legibility of the markings and effectively addressing interference factors such as paper wrinkles and stains. The transcoding module 13 uses algorithms to convert complex image information into a single marking matrix, reducing data redundancy and increasing information density. The decoding module 14 uses efficient decoding algorithms (such as error-correcting coding technology) to restore the complete printed information through matrix logic relationships, even if the markings are partially missing or blurred, ensuring recognition accuracy and fault tolerance. The system is optimized for the characteristics of laser-printed markings and electronic watermarks, adapting to different print quality (such as toner concentration fluctuations) and paper conditions (such as minor soiling). By analyzing the unique printed information contained in the special markings (such as timestamps, device numbers, and encryption identifiers), the system can accurately trace the document source and printing device, effectively curbing illegal copying.

[0052] In one embodiment, the scanning module 11 adopts a spectral scanner, the scanning imaging wavelength range of the spectral scanner is 320-1200nm, and the first image obtained by scanning is as follows: Figure 3 As shown. The spectral scanner is the aforementioned multispectral imaging device 100. The imaging wavelength range of the multispectral imaging device 100 is 200 to 1600 nm. Furthermore, the imaging wavelength range of the multispectral imaging device 100 is 200 to 1600 nm. Preferably, the light transmission wavelength range of the multispectral carrier glass 300 or the imaging wavelength of the multispectral imaging device 100 covers the wavelength range of visible light and part of infrared and ultraviolet light.

[0053] Furthermore, the scanning module 11 performs push-broom hyperspectral imaging or gaze-based snapshot imaging. Push-broom hyperspectral imaging utilizes a sample stage and precision leadscrew (1μm stepping accuracy) to achieve spatial movement, while grating rotation covers the entire spectral band. The VIS band operates at 500 fps at 512×512 pixels, and the NIR band at 200 fps (limited by the response speed of InGaAs). Gaze-based snapshot imaging utilizes an integrated liquid crystal tunable filter (LCTF) or acousto-optic tunable filter (AOTF), supporting a 10ms switching speed within a 10nm bandwidth. Ten preset characteristic bands (such as 350nm / 450nm / 550nm / 670nm / 850nm / 940nm / 1050nm) enable rapid multispectral imaging.

[0054] Furthermore, the scanning module 11 may also include a light source unit (e.g., an illuminating element 400). The wavelength range of light emitted by the illuminating element 400 is 300 to 600 nm. Preferably, the wavelength range of light emitted by the illuminating element 400 is 400 to 560 nm. The wavelength range of the illuminating element 400 is within the transmission wavelength range of the multispectral carrier glass 300 or the imaging wavelength range of the multispectral imaging device 100, and is primarily visible light used to enhance brightness. The incident angle of the illuminating element 400 is 0 to 90 degrees, and the incident angle can be adjusted automatically or manually by configuring an adjustment structure.

[0055] The lighting element 400 can be specifically a multi-band composite light source or a tunable pulse light source. The multi-band composite light source combines a deuterium lamp (covering the ultraviolet region of 320-400nm) with a halogen lamp (visible and near-infrared, 400-1200nm), using fiber coupling to achieve continuous spectrum output. Tunable pulse light sources integrate LED arrays or laser sources, supporting high-intensity excitation at specific wavelengths (e.g., 405nm / 785nm lasers for fluorescence enhancement).

[0056] The scanning module 11 may also include a spectrometer. This spectrometer utilizes a high-precision blazed grating (1200 lines / mm), achieving wavelength resolutions of 0.5nm-320nm and 1.2nm-1200nm. Prism-assisted compensation is employed in the spectrometer, with calcium fluoride prisms added in the ultraviolet region to compensate for dispersion and reduce interference from higher-order grating diffraction.

[0057] In one embodiment, the image processing module 12 is pre-set with an image pre-processing algorithm, including at least one of: dual-domain decomposition layer, retinal neural dynamic compression, phase-guided spatial domain reconstruction, and frequency-domain-spatial domain joint denoising.

[0058] The dual-domain decomposition layer decomposes the image into the frequency domain (frequency features) and the spatial domain (spatial features), processes them separately, and then fuses them to simultaneously optimize global structure and local details. Specifically, it uses a fast Fourier transform (FFT) to extract the image spectrum, separating high frequencies (edges, textures) from low frequencies (background, smooth areas). It then uses spatial gradient or wavelet transforms to extract local texture and structure information. Finally, it dynamically weights and fuses the frequency and spatial domain features to enhance key information.

[0059] Retinal Neural Dynamic Compression (RNDC) simulates the nonlinear response characteristics of retinal ganglion cells in the human eye and differentially processes high- and low-frequency components of an image. RDNDC decomposes the image into high-frequency components (details) and low-frequency components (background), enhancing the high-frequency components and dynamically compressing the low-frequency components.

[0060] Phase-Guided Spatial Reconstruction (Phase-Guided Spatial Reconstruction) utilizes traditional Fourier phase information, which only contains positional information. This algorithm introduces a dynamic phase field and incorporates local contrast features. Phase-Guided Spatial Reconstruction uses a convolutional neural network (CNN) to predict an optimized phase field. It takes local frequency features and a spatial gradient histogram as input, enhances edges and textures based on the phase information, and then fuses the enhanced texture with the original image.

[0061] Frequency-Spatial Joint Denoising combines frequency-domain soft threshold denoising with spatial non-local means (NLM) filtering. Frequency-Spatial Joint Denoising first applies a soft threshold filter to the image spectrum to suppress high-frequency noise, then applies a non-local means filter to the denoised image to preserve details.

[0062] In one embodiment, the second image is an electronically encoded image, such as Figure 6 、 7 As shown. After the above image preprocessing algorithm, the first image is converted into a single background image with markers. The markers are enhanced to show more clearly. The background is the first color and the markers are the second color. The first color and the second color are clearly distinguishable. For example Figure 7 shown.

[0063] In one embodiment, the second image is a narrowband spectral image, such as Figure 4 As shown. Preferably, the narrowband spectral image is a single spectral image or a narrowband spectral image. The image processing module 12 can convert the first image into the second image by spectral response inversion, calibrate the RGB spectral response curve of the camera (the sensitivity of RGB to each wavelength is known), and map the RGB value to the intensity of the target wavelength (such as 532nm) through the pseudo-inverse matrix or least squares method, and finally output a single spectrum image. Deep learning multispectral reconstruction can also be used. Specifically, using RGB-narrowband spectrum pairing data in a hyperspectral database (such as CAVE, ICVL), training networks such as U-Net and Transformer, learning the mapping from RGB to multi-band spectrum, inputting an RGB image, and outputting a predicted narrowband spectrum cube.

[0064] In one embodiment, the second image is a grayscale image. The image processing module 12 may convert the first image into a grayscale image using an average method, a weighted average method (brightness method), a maximum method, or a minimum method. The image processing module 12 may also use a grayscale enhancement algorithm, such as histogram equalization, adaptive histogram equalization, and gamma correction.

[0065] In one embodiment, the transcoding module 13 includes: an identification unit for identifying all the special marks of the first image or the second image and obtaining an overall mark matrix of the first image; and a division unit for extracting a single mark matrix representing printing information from the overall mark matrix.

[0066] Specifically, the overall marking matrix is a matrix of all marking point information. The recognition unit can establish a coordinate system in the first or second image and record the coordinate information of each marking point as a matrix value in the overall marking matrix. Because the hidden mark appears repeatedly in the sample in a matrix form, the recognition unit can also identify groups of marking points that appear repeatedly in the overall marking matrix. These repeated groups of marking points represent a type of printed information, i.e., a corresponding single marking matrix.

[0067] The transcoding module 13 can also automatically compare the determined multiple single mark matrices and determine whether the single mark matrix correctly represents the printed information through the codes generated by the first and last columns of the matrix. Figure 5 As shown, it is also possible to process the second image, for example Figure 6 、 7 As shown, a coordinate system is generated in the electronically encoded image.

[0068] In one embodiment, the recognition system further includes a grid module, which is used to divide the single marker matrix into grids so that each grid has a corresponding preset number of marker points, such as Figure 8 shown.

[0069] Specifically, the grid module can be a program running automatically within the recognition system, or it can be an interactive module provided to the user for operation. The grid module divides the multiple values in a single marking matrix into a preset grid size. Each grid is of the same size and contains a corresponding preset number of marking points. The preset number of marking points can be one or more, and each group of the preset number of marking points corresponds to a character or a number, which is subsequently converted into a character or number representing the printed information in the decoding module 14. Furthermore, as a preferred embodiment, the grid module automatically generates a grid based on the row and column spacing of the hidden markings. The user can adjust the grid size and increase or decrease the number of marking points. This facilitates user use, manual interpretation, and manual intervention.

[0070] Each module in the above-mentioned identification system can be implemented in whole or in part through software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in the computer device in the form of software, so that the processor can call and execute the corresponding operations of each module.

[0071] The decoding module 14 has a built-in decoding algorithm that matches different printing machines. After receiving the matrix data divided into grids, the decoding module 14 automatically interprets the corresponding code of the hidden mark and automatically generates printing information. The printing information contains the output device model information code, the output device output date information code, the output device output time code, etc.

[0072] In a second aspect, the present application provides a printed matter recognition method, the printed matter recognition method comprising the steps of:

[0073] S100, scanning a printed product printed by a printer to form a first image, wherein the printed product has a special mark;

[0074] S200: Preprocess the first image to obtain a second image;

[0075] S300, identifying the special mark in the first image or the second image to obtain a single mark matrix representing printed information;

[0076] S400: Decode the single marking matrix to obtain the printing information corresponding to the printed product.

[0077] In one embodiment, in the step of scanning the printed product printed by the printer to form a first image in S100, spectral scanning is used, and the scanning imaging wavelength range of the spectral scanning is 200-1600 nm.

[0078] In one embodiment, the step of S300, identifying the special mark in the first image or the second image to obtain a single mark matrix representing printed information, further includes:

[0079] S310: Identify all the special marks of the first image or the second image, and obtain an overall mark matrix of the first image or the second image;

[0080] S320: Extract a single mark matrix representing printing information from the overall mark matrix.

[0081] In one embodiment, the step S300 of identifying the special mark in the first image or the second image and obtaining a single mark matrix representing printed information further includes: S330 of dividing the single mark matrix into a grid so that each grid has a corresponding preset number of mark points.

[0082] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0083] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0084] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiments. In particular, any reference to memory, database, 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 various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may be, but are not limited to, general-purpose processors, central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), programmable logic devices (PLDs), data processing logic devices based on quantum computing, and the like.

[0085] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, 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.

[0086] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A color laser printer printout recognition system, characterized in that: The color laser printer print product recognition system includes: A scanning module, configured to scan a printed product printed by a printer to form a first image, wherein the printed product has a special mark; an image processing module, connected to the scanning module, and configured to pre-process the first image to obtain a second image; a transcoding module, connected to the image processing module or the scanning module, for identifying the special mark in the first image or the second image to obtain a single mark matrix representing printed information; and A decoding module is connected to the transcoding module and is used to interpret the single mark matrix to obtain the printing information corresponding to the printed product.

2. The color laser printer printout recognition system according to claim 1, characterized in that: The scanning module adopts a spectrum scanner, and the scanning imaging wavelength range of the spectrum scanner is 320-1200nm.

3. The color laser printer printout recognition system according to claim 1, characterized in that: The image processing module is preset with an image preprocessing algorithm, including at least one of: dual-domain decomposition layer, retinal neural dynamic compression, phase-guided spatial domain reconstruction, and frequency domain-spatial domain joint noise reduction.

4. The color laser printer printout recognition system according to claim 1, characterized in that: The second image is a narrowband spectral image.

5. The color laser printer printout recognition system according to claim 1, characterized in that: The transcoding module includes: an identification unit, configured to identify all the special marks of the first image or the second image and obtain an overall mark matrix of the first image; The dividing unit is used to extract a single mark matrix representing printing information from the overall mark matrix.

6. The color laser printer printout recognition system according to claim 1, characterized in that: The color laser printer printout recognition system further includes a grid module, which is used to divide the single marking matrix into grids so that each grid has a corresponding preset number of marking points.

7. A method for identifying printed matter of a color laser printer, characterized in that: The printed matter identification method comprises the steps of: Scanning a printed product printed by the printer to form a first image, wherein the printed product has a special mark; Preprocessing the first image to obtain a second image; Recognizing the special mark in the first image or the second image to obtain a single mark matrix representing printed information; The single mark matrix is interpreted to obtain the printing information corresponding to the printed product.

8. The method for identifying printed matter of a color laser printer according to claim 7, characterized in that: In the step of scanning the printed product printed by the printer to form the first image, spectral scanning is adopted, and the scanning imaging wavelength range of the spectral scanning is 320-1200nm.

9. The method for identifying printed matter of a color laser printer according to claim 7, characterized in that: The step of identifying the special mark in the first image or the second image to obtain a single mark matrix representing printed information further includes: identifying all the special marks of the first image or the second image, and obtaining an overall mark matrix of the first image or the second image; A single mark matrix representing printing information is extracted from the overall mark matrix.

10. The method for identifying printed matter of a color laser printer according to claim 7, characterized in that: After the step of identifying the special mark in the first image or the second image to obtain a single mark matrix representing printed information, the method further includes: dividing the single mark matrix into grids so that each grid has a corresponding preset number of mark points.

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