Paper archive digitization method and system based on bar codes

Abstract

The invention discloses an emergency equipment health state monitoring and early warning method and device based on data analysis, and the method comprises the steps: starting a multispectral imaging system according to the archive age and material characteristics, and synthesizing multi-channel information into a high-fidelity digital image through a spectrum fusion algorithm; carrying out character detection on the scanned image according to the CRNN neural network model, and carrying out correction and enhancement on characters; generating a bar code according to the data of the archive; generating a self-adaptive watermark according to the archive image; storing the aggregated multi-dimensional hash value according to the block chain; and the consistency of the block chain evidence and the local data is verified, the authenticity of the evidence is verified, and the security of the digital paper archive is ensured. Through multi-dimensional hash value aggregation and timestamp binding, existence, integrity and sequential proof of strong legal efficacy is provided for electronic archives.

Description

Technical Field

[0001] This invention belongs to the field of image processing technology, specifically relating to a method and system for digitizing paper archives based on barcodes. Background Technology

[0002] With the development of information technology, archival management is undergoing a transformation from physical management to digital management. However, as the most original primary sources, paper archives have irreplaceable preservation value, and most institutions still retain a large number of paper archives. These paper archives are usually bound into volumes and stored in boxes, facing problems such as low management efficiency, potential for tampering, and difficulty in retrieval.

[0003] Common record management technologies mainly include record detection and identification technologies based on barcodes, QR codes, or RFID. Barcode technology consists of a set of lines of varying thicknesses, in black and white or other colors, along with corresponding characters and numbers, used to represent certain information.

[0004] Currently, existing barcode-based record management systems still have many limitations. First, their tamper-proof capabilities are weak; traditional barcode systems struggle to prevent alteration of record content, and if barcodes are copied or forged, it can lead to serious security problems. Second, the retrieval efficiency of existing systems needs improvement, particularly their ability to quickly locate specific content within a large volume of records. Furthermore, traceability is a significant problem with existing systems; when records are tampered with or damaged, it is difficult to track the operational history and identify responsible parties.

[0005] Current technologies lack a paper-based digitization system that effectively combines barcodes, digital watermarking, and blockchain technologies, failing to simultaneously address issues such as tamper-proofing, rapid retrieval, and reliable traceability. Therefore, there is an urgent need for an innovative solution that fully leverages the advantages of these advanced technologies to build a secure, efficient, and reliable paper-based digitization management system. Summary of the Invention

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for digitizing paper archives based on barcodes, the method comprising the following steps: Step S1: Activate the multispectral imaging system based on the age and material characteristics of the archives, and synthesize multi-channel information into a high-fidelity digital image through a spectral fusion algorithm; Step S2: Detect characters in the scanned image using the CRNN neural network model, and then correct and enhance the characters. Step S3: Generate a barcode based on the data in the archive; Step S4: Generate an adaptive watermark based on the archive image; Step S5: Store the aggregated multi-dimensional hash values ​​as evidence based on the blockchain; Step S6: Verify the consistency between the blockchain-based evidence and the local data, verify the authenticity of the evidence, and ensure the security of the digitized paper archives.

[0007] Furthermore, the multi-channel information includes: the visible light channel uses a high-precision CCD sensor to independently acquire RGB three channels, and the infrared channel uses an InGaAs detector to detect potential watermarks and invisible marks.

[0008] Furthermore, the correction enhancement includes identifying the text baseline angle through a connection classification algorithm, comprehensively calculating the image tilt angle by combining the Hough transform line detection results, and performing subpixel-level precision rotation correction using bicubic interpolation affine transformation.

[0009] Furthermore, generating barcodes based on archive data includes: S31, connecting to the archive management system API to automatically extract metadata from the base archive; generating a first-level barcode based on the metadata hash value, and simultaneously establishing a hash value-metadata association index; S32. Start the OCR engine to scan the document cover and title page area, recognize and supplement handwritten annotations and special markings; S33. Implement a block hashing strategy for the cover image of the digitized archive, divide the large file into 1MB data blocks, calculate SHA-256 in parallel, combine the hash values ​​of each block, and use the time-based HMAC algorithm to generate the hash value of the dynamic and secure overall file based on the timestamp. S34. Generate a secondary barcode based on the hash value of the entire file.

[0010] Furthermore, generating adaptive watermarks based on archival images includes, S41. Analyze the character area ratio and non-white pixel area ratio on page i to determine the pixel density level of page i. S42. Determine the watermark area and watermark position based on the pixel density level of page i. S43. Generate a digital watermark based on the watermark area parameter, and embed the digital watermark and the secondary barcode into the watermark position on the i-th page.

[0011] Furthermore, determining the watermark area and watermark position based on the pixel density level of page i includes: S421. Determine the watermark area based on pixel density levels, where the length and width of the watermark are less than the minimum value of the minimum width and minimum length of the border of the i-th page. S422. Determine the position based on the pixel density level, where the higher the pixel level, the closer the watermark position is to the bottom of the i-th page; S423. Statistically analyze the distribution of watermark pixel density levels and watermark position distribution on page i to form a first sequence of page number-watermark pixel density levels and a second sequence of page number-watermark position distribution. S424. Generate a document verification code based on the first sequence and the second sequence. The document verification code is located on the electronic homepage.

[0012] Furthermore, the storage of aggregated multi-dimensional hash values ​​based on the blockchain includes: S51. Extract the watermark data generated by the digital watermark module and calculate the hash value of the watermark information; S52. Aggregate the metadata hash value, the hash value of the dynamic security overall file, and the watermark information hash value, add a timestamp and a random salt value, and generate the target hash value. S53. Encapsulate the evidence information into a standardized format that can be recognized by the blockchain.

[0013] Furthermore, the evidence storage information includes: unique file identifier, target hash value, hash value of dynamic security overall file, metadata hash value, watermark information hash value, evidence storage operator, evidence storage time, block height, and previous evidence storage hash value.

[0014] A barcode-based paper archive digitization system, comprising the following modules: The interconnected scanning module, image enhancement module, multi-level barcode generation module, digital watermarking module, blockchain evidence storage module, and verification and query module; The scanning module is used to synthesize multi-band information into a high-fidelity digital image through a spectral fusion algorithm; The image enhancement module is used to perform character line detection on the scanned image and enhance the characters; A multi-level barcode generation module is used to generate barcodes based on archival data; The digital watermarking module is used to generate adaptive watermarks based on archival images; The blockchain evidence storage module is used to calculate and store multi-dimensional hash value aggregation. The verification and query module is used to verify the consistency between blockchain-stored evidence and local data.

[0015] The beneficial effects of this invention are as follows: This invention utilizes a multispectral imaging system, activated based on the archival period and material characteristics, to synthesize high-fidelity digital images from multiple channels using a spectral fusion algorithm. It then employs a CRNN neural network model to detect and enhance characters within the scanned images. Furthermore, it generates barcodes based on archival data, creates adaptive watermarks from the archival images, and stores aggregated multi-dimensional hash values ​​using blockchain technology. Finally, it verifies the consistency between the blockchain-stored evidence and local data, ensuring the authenticity of the evidence and guaranteeing the security of digitized paper archives. This invention deeply integrates four security measures: primary barcodes (metadata), secondary barcodes (document fingerprints), adaptive digital watermarks, and blockchain-based evidence storage. The adaptive watermark dynamically adjusts according to content density, significantly increasing the difficulty of tampering while maintaining visual appeal. The aggregation of multi-dimensional hash values ​​and their binding with timestamps provide legally valid proof of existence, integrity, and timeliness for electronic archives, meeting the highest requirements for long-term secure preservation and compliant auditing of archives.

[0016] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention, it can be implemented according to the contents of the specification. In order to make the above description and other objects, features and advantages of the present invention more obvious and understandable, preferred embodiments are provided and described in detail below. Attached Figure Description

[0017] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0018] Figure 1 A flowchart of a barcode-based method for digitizing paper archives. Detailed Implementation

[0019] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0020] In the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "joining," "fixing," etc., should be interpreted broadly. For example, they can refer to a connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0021] Example 1 A method for digitizing paper archives based on barcodes, comprising the following steps: Step S1: Activate the multispectral imaging system based on the age and material characteristics of the archives, and synthesize multi-channel information into a high-fidelity digital image through a spectral fusion algorithm; Step S2: Detect characters in the scanned image using the CRNN neural network model, and then correct and enhance the characters. Step S3: Generate a barcode based on the data in the archive; Step S4: Generate an adaptive watermark based on the archive image; Step S5: Store the aggregated multi-dimensional hash values ​​as evidence based on the blockchain; Step S6: Verify the consistency between the blockchain-based evidence and the local data, verify the authenticity of the evidence, and ensure the security of the digitized paper archives.

[0022] Furthermore, the multi-channel information includes: the visible light channel uses a high-precision CCD sensor to independently acquire RGB three channels, and the infrared channel uses an InGaAs detector to detect potential watermarks and invisible marks.

[0023] Furthermore, the correction enhancement includes identifying the text baseline angle through a connection classification algorithm, comprehensively calculating the image tilt angle by combining the Hough transform line detection results, and performing subpixel-level precision rotation correction using bicubic interpolation affine transformation.

[0024] Furthermore, generating barcodes based on archive data includes: S31, connecting to the archive management system API to automatically extract metadata from the base archive; generating a first-level barcode based on the metadata hash value, and simultaneously establishing a hash value-metadata association index; S32. Start the OCR engine to scan the document cover and title page area, recognize and supplement handwritten annotations and special markings; S33. Implement a block hashing strategy for the cover image of the digitized archive, divide the large file into 1MB data blocks, calculate SHA-256 in parallel, combine the hash values ​​of each block, and use the time-based HMAC algorithm to generate the hash value of the dynamic and secure overall file based on the timestamp. S34. Generate a secondary barcode based on the hash value of the entire file.

[0025] Furthermore, generating adaptive watermarks based on archival images includes, S41. Analyze the character area ratio and non-white pixel area ratio on page i to determine the pixel density level of page i. S42. Determine the watermark area and watermark position based on the pixel density level of page i. S43. Generate a digital watermark based on the watermark area parameter, and embed the digital watermark and the secondary barcode into the watermark position on the i-th page.

[0026] Furthermore, determining the watermark area and watermark position based on the pixel density level of page i includes: S421. Determine the watermark area based on pixel density levels, where the length and width of the watermark are less than the minimum value of the minimum width and minimum length of the blank border area on the i-th page. S422. Determine the position based on the pixel density level, where the higher the pixel level, the closer the watermark position is to the bottom of the i-th page; The pixel density level is divided into 10 levels, and the watermark position is divided into 10 positions from the bottom to the top of the page. If the pixel density level is 10, the watermark position is at position 1, which is close to the bottom of the page; if the pixel density level is 9, the watermark position is at position 2, which is close to the second bottom of the page; and so on. S423. Statistically analyze the distribution of watermark pixel density levels and watermark position distribution on page i to form a first sequence of page number-watermark pixel density levels and a second sequence of page number-watermark position distribution. S424. Generate a document verification code based on the first sequence and the second sequence. The document verification code is located on the electronic homepage.

[0027] Furthermore, by analyzing the proportion of character area and non-white pixel area on page i, the pixel density level of page i is determined, including: S411. If Ii is a color image, convert it to a grayscale image Igray.

[0028] Igray = 0.299R + 0.587G + 0.114B; S412. Use local adaptive binarization to process Igray to highlight the text region. T(x,y)=m(x,y)×[1+k×(s(x,y) / (R-1)] Where m(x,y) is the local mean, s(x,y) is the local standard deviation, k is the correction parameter, and R is the dynamic range of the standard deviation. Calculate the total area A of the text pixels in the binary image BW.text ; BW(x,y)=1 if I gray (x,y)>T(x,y); BW(x,y)=0 if I gray (x,y)≤T(x,y); S413, Calculate text region density D text =A total / A text ×100%, A total This represents the total number of pixels in the image. S414. After calculating the pixel density score, perform pixel density level mapping. ; α, β: Weighting exponents. α = 1.2, β = 0.8. S i This represents the pixel density score on the i-th page;

[0029] f() represents the floor function. L i =min(10, max(1, L) i The min() function takes the minimum value, and the max() function takes the maximum value. The pixel density level can be obtained by following the above method.

[0030] Furthermore, the storage of aggregated multi-dimensional hash values ​​based on the blockchain includes: S51. Extract the watermark data generated by the digital watermark module and calculate the hash value of the watermark information; S52. Aggregate the metadata hash value, the hash value of the dynamic security overall file, and the watermark information hash value, add a timestamp and a random salt value, and generate the target hash value. S53. Encapsulate the evidence information into a standardized format that can be recognized by the blockchain.

[0031] Furthermore, the evidence storage information includes: unique file identifier, target hash value, hash value of dynamic security overall file, metadata hash value, watermark information hash value, evidence storage operator, evidence storage time, block height, and previous evidence storage hash value.

[0032] Verify the consistency between blockchain-based evidence and local data, verify the authenticity of the evidence, and ensure the security of digitized paper archives. This includes: S61, querying the corresponding evidence storage record from the blockchain network based on the unique identifier of the file; including the target hash value, metadata hash value, watermark information hash value, and the hash value of the dynamic security overall file; S62. Recalculate the multi-dimensional hash values ​​of local data, including metadata hash values, watermark information hash values, hash values ​​of the dynamic security overall file, and target hash values. S63. Compare the target hash value in the evidence storage record with the recalculated target hash value of the local data. If they match, the verification is successful. Otherwise, compare the metadata hash value, watermark information hash value, and dynamic security overall file hash value in sequence to find the modification information. S64. Extract the digital watermark embedded in the archive image. Based on the watermark extraction result, recalculate the document verification code and compare it with the document verification code on the first page of the document. If they are the same, it proves that the watermark has not been tampered with. If they are inconsistent, it indicates that the archive image may have been tampered with or destroyed. S65. Periodically poll the blockchain network to check the status of the evidence storage. If an update to the evidence storage is detected, automatically synchronize it to the local index, record all verification and synchronization operation logs, and generate an audit report, including verification time, file identifier, operator, verification result, and synchronization event.

[0033] Example 2 A barcode-based paper archive digitization system, comprising the following modules: The interconnected scanning module, image enhancement module, multi-level barcode generation module, digital watermarking module, blockchain evidence storage module, and verification and query module; The scanning module is used to synthesize multi-band information into a high-fidelity digital image through a spectral fusion algorithm; The image enhancement module is used to perform character line detection on the scanned image and enhance the characters; A multi-level barcode generation module is used to generate barcodes based on archival data; The digital watermarking module is used to generate adaptive watermarks based on archival images; The blockchain evidence storage module is used to calculate and store multi-dimensional hash value aggregation. The verification and query module is used to verify the consistency between blockchain-stored evidence and local data.

[0034] The advantages of this invention are as follows: 1. A multi-layered, integrated anti-counterfeiting and integrity verification system has been constructed, deeply integrating four security measures: primary barcode, secondary barcode, adaptive digital watermark, and blockchain evidence storage. The adaptive watermark dynamically adjusts according to content density, greatly enhancing the difficulty of tampering while ensuring visual appeal; while blockchain technology ensures the non-repudiation and global verifiability of all verification credentials (hash values), forming a three-dimensional protection of "locally verifiable (barcode, watermark) and globally auditable (blockchain)".

[0035] 2. The verification and query module can quickly compare local data with on-chain evidence, and any subtle tampering can be quickly located and alerted. This allows archive managers, users, or auditors to verify the authenticity and integrity of the digital archives they obtain anytime, anywhere, without relying on the original system, greatly enhancing the trust value and circulation capability of digital archives.

[0036] 3. Digital watermarking uses content-based adaptive embedding. In complex background areas, embedding high-density watermarks may cause significant visual interference; in simple background areas, embedding low-density watermarks is sufficient. The adaptive strategy achieves the optimal balance between robustness and invisibility.

[0037] While the specific embodiments of this disclosure have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of this disclosure. Those skilled in the art should understand that various modifications or alterations that can be made by those skilled in the art without creative effort based on the technical solutions of this disclosure are still within the scope of protection of this disclosure.

Claims

1. A bar code based paper archive digitization system, characterized by, The system comprises the following modules: The scanning module, the image enhancement module, the multi-level barcode generation module, the digital watermarking module, the blockchain storage module, and the verification and query module are connected with each other; The scanning module is used for fusing multi-band information into a high-fidelity digital image through a spectral fusion algorithm; The image enhancement module is used for character line detection and character enhancement on the scanned image; The multi-level barcode generation module is used for generating a barcode according to the archive data; The digital watermarking module is used for generating adaptive watermarking according to the archive image; The blockchain storage module is used for calculating multi-dimensional hash values, aggregating and storing the hash values; The verification and query module is used for verifying the consistency of the blockchain storage and the local data.

2. A method for digitizing paper archives based on barcodes, characterized in that, The method comprises the following steps: Step S1, starting a multi-spectral imaging system according to the archive age and material characteristics, and fusing multi-channel information into a high-fidelity digital image through a spectral fusion algorithm; Step S2, detecting characters on the scanned image according to a CRNN neural network model, and correcting and enhancing the characters; Step S3, generating a barcode according to the archive data; Step S4, generating adaptive watermarking according to the archive image; Step S5, storing the aggregated multi-dimensional hash values according to the blockchain; Step S6, verifying the consistency of the blockchain storage and the local data, verifying the storage authenticity, and ensuring the security of the digitized paper archives.

3. The method of claim 1, wherein: The multi-channel information comprises: visible light channels are independently collected by high-precision CCD sensors in RGB three channels, and infrared channels use InGaAs detectors to detect potential watermarks and invisible markers.

4. The method of claim 1, wherein: The correction and enhancement comprises: identifying the text baseline angle through a connection classification algorithm, comprehensively calculating the image tilt angle by combining the Hough transform straight line detection result, and performing sub-pixel level precision rotation correction by using a bi-cubic interpolation affine transformation.

5. The paper archive digitization method based on barcodes according to claim 1, characterized in that: Generating a barcode according to the archive data comprises: S31, connecting an archive management system API to automatically extract the metadata of the base archive; based on the metadata hash value, a first-level barcode is generated according to the metadata hash value, and a hash value-metadata association index is established; S32, starting an OCR engine to scan the archive cover and title page area, and identifying and supplementing handwritten notes and special marker information; S33, implementing a block hashing strategy on the digitized archive title page image, dividing the large file into 1MB data blocks for parallel SHA-256 calculation, combining the block hash values, and generating a dynamic security overall file hash value according to the timestamp by using a time-based HMAC algorithm; S34, generating a second-level barcode according to the overall file hash value.

6. The method of claim 1, wherein: Generating adaptive watermarking according to the archive image comprises: S41, analyzing the character area ratio and non-white pixel area ratio of the ith page to determine the pixel density level of the ith page, S42, determining the watermark area and watermark position according to the pixel density level of the ith page; S43, generating a digital watermark according to the watermark area parameter, and embedding the digital watermark and the second-level barcode into the watermark position of the ith page.

7. The method of claim 6, wherein: Determining the watermark area and watermark position according to the pixel density level of the ith page comprises: S421, determining the watermark area based on the pixel density level, wherein the length and width of the watermark are smaller than the minimum value of the minimum width and the minimum length of the border of the i-th page, S422, determining the position based on the pixel density level, wherein the higher the pixel level, the closer the watermark position to the bottom of the i-th page; S423, counting the distribution of the watermark pixel density level and the distribution of the watermark position in the i-th page to form a first sequence of the page number-watermark pixel density level and a second sequence of the page number-watermark position, S424, generating a document verification code based on the first sequence and the second sequence, and the document verification code is located in the electronic front page.

8. The method of claim 1, wherein: According to the blockchain, the aggregated multi-dimensional hash value is notarized, which includes: S51, extracting the watermark data generated by the digital watermark module to calculate the watermark information hash value; S52, aggregating the metadata hash value, the hash value of the dynamic security whole file, and the watermark information hash value, adding a timestamp and a random salt value to generate a target hash value; S53, encapsulating the notarization information into a standardized format recognizable by the blockchain.

9. The method of claim 8, wherein: Wherein, The notarization information includes a unique archive identifier, a target hash value, a hash value of a dynamic security whole file, a metadata hash value, a watermark information hash value, a notarization operator, a notarization time, a block height, and a previous notarization hash value.

Images