System and method for digitally and intelligently identifying contract

Abstract

The invention provides a system and method for digitally and intelligently recognizing a contract, and belongs to the technical field of character recognition, and the system comprises a data acquisition module which is used for obtaining an initial image of a target contract through image acquisition equipment, and preprocessing the initial image to obtain a standard image in an electronic copy format; the intelligent recognition module is used for recognizing the image text content of the standard image by using an OCR tool and converting the image text content into original text data; the text processing module is used for performing word processing on the original text data to obtain standard text data of the target contract; and the text analysis module is used for analyzing the standard text data by using a natural language recognition technology, extracting key contract data and integrating the key contract data into formatted contract data with a standard structure. Contract recognition efficiency and accuracy are improved, and a system recognition process is optimized.

Description

Technical Field

[0001] The present invention relates to the technical field of text recognition, and in particular to a system and method for digital intelligent contract recognition. Background Art

[0002] At present, with the development of image recognition text technology, contracts are important transaction documents for enterprises. However, the recognition of contract images and the extraction of key elements usually require a lot of manpower for extraction and identification, and the labor cost is also high, which leads to low recognition efficiency when processing contracts.

[0003] Therefore, the present invention proposes a system and method for digital intelligent contract identification. Summary of the invention

[0004] The present invention provides a system and method for digital intelligent contract recognition, which is used to convert physical contracts into electronic formats by using image scanning technology, then recognize the text in the image according to the OCR tool, and then analyze the pre-processed text to extract the contract terms and integrate them into structured data, thereby improving the efficiency and accuracy of contract recognition and optimizing the system recognition process.

[0005] In one aspect, the present invention provides a system for digitally intelligently identifying contracts, comprising: Data acquisition module: using image acquisition equipment to acquire the initial image of the target contract, and pre-processing the initial image to obtain a standard image in the electronic copy format; Intelligent recognition module: Use OCR tools to recognize the image text content of standard images and convert the image text content into original text data; Text processing module: performs text processing on the original text data to obtain the standard text data of the target contract; Text analysis module: Use natural language recognition technology to analyze standard text data, extract key contract data, and integrate the key contract data into formatted contract data with a standard structure.

[0006] On the other hand, the data acquisition module includes: Equipment configuration unit: Determine the type of image acquisition equipment based on the standard image requirements of the recognition contract, select a standard shooting environment based on the surrounding environment parameters acquired by the sensor, and perform installation and configuration based on the installation manual of the image acquisition equipment; Image acquisition unit: used to acquire the initial image of the target contract using the installed image acquisition device.

[0007] On the other hand, the data acquisition module includes: A preprocessing unit: performing image enhancement on the initial image to obtain a clear image in an electronic copy format, and adjusting the clear image according to a preset standard ratio to obtain a first image; Correction unit: performs horizontal correction processing on the text part of the first image, and calls a blank area recognition algorithm to crop the area to be cropped, so as to obtain a second image; Compression unit: compresses the second image to a standard image size using a compression tool to obtain a standard image of the target contract.

[0008] On the other hand, the intelligent identification module includes: The first processing unit is used to extract the features of the standard image by using an OCR tool after the standard image is loaded to generate a first feature image. The second processing unit: rotates the first feature image at 8 angles to obtain directional feature images at 8 angles, and extracts features from the 8 directional feature images to obtain 8 directional feature values; Vector acquisition unit: converting each directional feature value into a corresponding directional vector according to a vector conversion standard, and taking the feature vector of the first feature image as a center vector; Matrix construction unit: constructs a covariance matrix based on all direction vectors and the center vector; Projection unit: obtains a mixed eigenvector according to the covariance matrix, projects the mixed eigenvector onto each direction vector to obtain a projection coefficient, and uses the corresponding projection coefficient as a weight coefficient of the corresponding directional feature image.

[0009] On the other hand, the intelligent identification module further includes: Fusion unit: performing feature fusion on 8 directional vectors based on the weight coefficient of the directional feature image to obtain a fused feature vector, wherein the fused feature vector includes a plurality of element features, and each element feature includes a horizontal sub-feature and a vertical sub-feature; Matching unit: matching the fused feature vector based on the feature-text library to obtain the text feature corresponding to each element feature in the fused feature vector; Calculate the initial similarity between each element feature and the corresponding matching text feature respectively; ; in, represents the initial similarity between the i-th element feature in the fused feature vector and the corresponding matching text feature; Represents the horizontal gradient difference between the i-th element feature in the fused feature vector and the corresponding text feature. Represents the vertical gradient difference between the i-th element feature in the fused feature vector and the corresponding text feature. ) represents the tangent function, and Respectively represent the i-th element feature and the corresponding text feature in the fused feature vector, Indicates based on The dot product function of express Norm ratio; Correcting the initial similarity to obtain a final similarity of the corresponding element features; ; in, Represents the final similarity of the i-th element feature in the fused feature vector, ) represents the exponential function, Indicates the maximum matching similarity of all element features in the fused feature vector. Indicates the minimum matching similarity of all element features in the fused feature vector. Represents the mean similarity of all element features in the fused feature vector; Indicates that based on all The variance of The mean of the variances of Based on the final similarity of each element feature in the fused feature vector, a corresponding text conversion scheme is matched from a similarity-text conversion mapping table to convert the corresponding image text content into original text data.

[0010] On the other hand, the text processing module includes: Cleaning unit: traverses the original text data, marks unrecognized words in the original text data as target words, and compares the target words with the standard dictionary; If there is a standard similar word in the target word, the target word is converted into a standard similar word with the highest similarity; otherwise, the target word is determined to be an unknown word, and a deletion operation is performed, and the first contract data is output after traversal; Other format units: filter and convert the unstructured data in the original text data in combination with the preset target text format, and output the second contract data; A text proofreading unit: based on a proofreader, corrects misspelled words in the first contract data and the second contract data to obtain standard text data.

[0011] On the other hand, the text analysis module includes: Word segmentation unit: used to segment standard text data into word sequences, and filter out useless stop words based on a general word dictionary to obtain a standard word sequence; Entity recognition unit: Based on the contract entity library, the named entity recognition system is used to identify entities of standard word sequences to obtain entity sequences; Keyword extraction unit: based on the contract keyword recognition library, extract keywords from the standard word sequence to obtain a keyword sequence; Judgment unit: Take the union of the entity sequence and the keyword sequence, filter out the useless words in the union, and obtain the key contract data.

[0012] On the other hand, the text analysis module includes: Syntax extraction unit: performs grammatical analysis on key contract data, determines the relationship and hierarchy between terms, and adds grammatical terms in a grammatical logical order to generate contract terms; Structured data unit: standardize the contract terms according to the format of the standard preset contract structure to obtain the formatted contract data of the target contract.

[0013] On the other hand, a method for digitally intelligently identifying contracts includes: Step 1: Use an image acquisition device to acquire an initial image of the target contract, and pre-process the initial image to obtain a standard image in the electronic copy format; Step 2: Use OCR tools to recognize the image text content of the standard image and convert the image text content into original text data; Step 3: Perform text processing on the original text data to obtain the standard text data of the target contract; Step 4: Use natural language recognition technology to analyze standard text data, extract key contract data, and integrate the key contract data into formatted contract data with a standard structure.

[0014] The present invention provides a system and method for digital intelligent contract recognition, which is used to convert physical contracts into electronic format by using image scanning technology, then recognize the text in the image according to the OCR tool, and finally use NLP technology to analyze the pre-processed text, extract the contract terms and integrate them into structured data, thereby improving the efficiency and accuracy of contract recognition and optimizing the system recognition process. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0016] Figure 1It is a schematic diagram of the structure of a digital intelligent contract recognition system provided by an embodiment of the present invention; Figure 2 It is a flow chart of a method for digital intelligent contract identification provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] Embodiment 1: like Figure 1 As shown, a digital intelligent contract recognition system provided by an embodiment of the present invention includes: Data acquisition module: using image acquisition equipment to acquire the initial image of the target contract, and pre-processing the initial image to obtain a standard image in the electronic copy format; Intelligent recognition module: Use OCR tools to recognize the image text content of standard images and convert the image text content into original text data; Text processing module: performs text processing on the original text data to obtain the standard text data of the target contract; Text analysis module: Use natural language recognition technology to analyze standard text data, extract key contract data, and integrate the key contract data into formatted contract data with a standard structure.

[0019] In this embodiment, the image acquisition device is any device used to photograph or scan documents, such as a scanner, a camera, or a webcam.

[0020] In this embodiment, the target contract refers to a specific contract document that needs to be processed and analyzed, and may be various types of legal contracts, commercial contracts, or other types of agreement documents.

[0021] In this embodiment, the initial image refers to a basic image of the target contract acquired from an image acquisition device, and the image includes text, graphics, and other elements.

[0022] In this embodiment, the preprocessing is to perform a series of operations on the initial image, including removing noise, adjusting the contrast and brightness of the image, and cropping or rotating the image.

[0023] In this embodiment, the electronic copy format refers to a standard format for converting a paper document into an electronic form, such as PDF, image files (such as JPEG, PNG), etc.

[0024] In this embodiment, the standard image refers to an image that has been pre-processed and meets specific format and quality standards, and is used for subsequent OCR processing.

[0025] In this embodiment, the OCR tool is a technical tool that scans an image and converts the text therein into editable text.

[0026] In this embodiment, the image text content refers to all text information displayed in the image. In the contract scan image, the text content includes all printed or handwritten text such as contract terms, title, signature, date, etc.

[0027] In this embodiment, the original text data refers to the unprocessed text content recognized from the image, including all the text in the contract, whether it is terms, titles, dates, signatures, etc.

[0028] In this embodiment, text processing refers to various operations and processing performed on raw text data, including text cleaning, text segmentation, and removal of stop words.

[0029] In this embodiment, standard text data is text data processed by a text processing module, has a certain format and structure, and is easy to further analyze and process. The characteristics include: clear readability, semantic accuracy, noise removal, etc.

[0030] In this embodiment, natural language recognition technology includes: text analysis, named entity recognition, sentiment analysis, semantic understanding and other technical contents.

[0031] In this embodiment, key contract data refers to data of great significance in the contract, including: contract terms and conditions, date and time, party information, amount and fees, agreed matters, etc.

[0032] In this embodiment, the standard structure refers to a unified format preset for the contract identification text.

[0033] In this embodiment, the formatted contract data is data obtained by arranging and processing the contract text data according to a standard structure.

[0034] The working principle and beneficial effects of the above technical solution are: using image acquisition equipment to acquire contract images, using OCR tools to recognize text, and then performing text processing and analysis to convert contract data into structured formatted data, thereby improving the efficiency and accuracy of contract text recognition and realizing the automation and intelligence of contract management.

[0035] Embodiment 2: Based on the above embodiment 1, the data acquisition module includes: Equipment configuration unit: Determine the type of image acquisition equipment based on the standard image requirements of the recognition contract, select a standard shooting environment based on the surrounding environment parameters acquired by the sensor, and perform installation and configuration based on the installation manual of the image acquisition equipment; Image acquisition unit: used to acquire the initial image of the target contract using the installed image acquisition device.

[0036] In this embodiment, the standard image requirements include: clarity, resolution, contrast, angle, direction and other requirements.

[0037] In this embodiment, the sensor is a sensor for acquiring various parameters in the image acquisition device, including: a light sensor, a temperature sensor, a humidity sensor, a positioning sensor and other sensors.

[0038] In this embodiment, the surrounding environment parameters refer to environmental factors that affect the quality and stability of image acquisition, including: light intensity, light source type, temperature, environmental color and other parameters.

[0039] In this embodiment, the standard shooting environment refers to a set of environmental conditions that meet the working requirements of the image acquisition device, including: appropriate light conditions, stable background, suitable temperature and humidity, appropriate shooting angle and distance, etc.

[0040] In this embodiment, the installation manual refers to the instruction manual or guide of the image acquisition device, and the contents include: device overview, installation requirements, installation steps, environment configuration, device calibration, etc.

[0041] The working principle and beneficial effects of the above technical solution are: by configuring appropriate image acquisition equipment and environment, the accuracy and efficiency of contract recognition are improved, the image processing process is optimized, and the error rate is reduced.

[0042] Embodiment 3: Based on the above embodiment 1, the data acquisition module includes: A preprocessing unit: performing image enhancement on the initial image to obtain a clear image in an electronic copy format, and adjusting the clear image according to a preset standard ratio to obtain a first image; Correction unit: performs horizontal correction processing on the text part of the first image, and calls a blank area recognition algorithm to crop the area to be cropped, so as to obtain a second image; Compression unit: compresses the second image to a standard image size using a compression tool to obtain a standard image of the target contract.

[0043] In this embodiment, image enhancement refers to a series of image processing technologies, including: improving image quality, enhancing image features, removing noise from images, and other operations.

[0044] In this embodiment, a clear image refers to an image with high quality, clear details and bright colors after image enhancement processing.

[0045] In this embodiment, the preset standard ratio is a pre-set size ratio of the contract image, such as the aspect ratio of the image.

[0046] In this embodiment, the first image is an image obtained after being processed by the preprocessing unit and adjusted according to a preset standard ratio.

[0047] In this embodiment, the horizontal correction processing refers to adjusting the text portion in the image so that it is arranged or aligned horizontally, including operations such as rotation and text distortion adjustment.

[0048] In this embodiment, the blank area recognition algorithm is an algorithm for detecting blank areas in an image. A blank area is an area without text or content. The algorithm is implemented based on techniques such as pixel value, edge detection, and area segmentation of the image.

[0049] In this embodiment, the area to be cropped refers to an area that is identified as needing to be cropped during processing by the correction unit.

[0050] In this embodiment, the second image is the first image processed by the correction unit.

[0051] In this embodiment, the compression tool is various image compression software or tools, and common compression tools include Adobe Photoshop, GIMP and the like.

[0052] The working principle and beneficial effects of the above technical solution are: through the three steps of preprocessing, correction and compression, the quality and clarity of the contract image are improved, irrelevant information is removed, and finally a standardized image is obtained, which helps to improve the accuracy of text recognition and the efficiency of subsequent processing.

[0053] Embodiment 4: Based on the above embodiment 1, the intelligent identification module includes: The first processing unit is used to extract the features of the standard image by using an OCR tool after the standard image is loaded to generate a first feature image. The second processing unit: rotates the first feature image at 8 angles to obtain directional feature images at 8 angles, and extracts features from the 8 directional feature images to obtain 8 directional feature values; Vector acquisition unit: converting each directional feature value into a corresponding directional vector according to a vector conversion standard, and taking the feature vector of the first feature image as a center vector; Matrix construction unit: constructs a covariance matrix based on all direction vectors and the center vector; Projection unit: obtains a mixed eigenvector according to the covariance matrix, projects the mixed eigenvector onto each direction vector to obtain a projection coefficient, and uses the corresponding projection coefficient as a weight coefficient of the corresponding directional feature image.

[0054] In this embodiment, the recognition algorithm is an algorithm for extracting image features, including: scale-invariant feature transformation, oriented gradient histogram, convolutional neural network, principal component analysis and other algorithmic technologies.

[0055] In this embodiment, the features of the image refer to relevant information in the image extracted and processed by the processing unit, including texture, shape, edge, color distribution, etc. of the image.

[0056] In this embodiment, the first feature image is an image feature representation extracted by a recognition algorithm after a standard image is loaded through an OCR tool.

[0057] In this embodiment, the directional characteristic image refers to an image obtained by rotating the first characteristic image to 8 different angles. Each directional characteristic image represents the characteristic performance of the original image at different angles.

[0058] In this embodiment, feature extraction is a technology for extracting image features in image processing, such as edge detection, histogram statistics, Fourier transform, etc.

[0059] In this embodiment, the directional feature value refers to the feature value extracted from the feature image after each angle rotation, including features such as edges, textures, corner points, etc. that can be represented by numerical values.

[0060] In this embodiment, the vector conversion standard refers to the standard used in the process of converting the eigenvalue into the direction vector. The conversion method may, for example, use a simple normalization method to map the eigenvalue into a unit vector.

[0061] In this embodiment, the center vector is a feature vector extracted from the first feature image. For example, the center image A is , the vector is .

[0062] In this embodiment, the direction vector is a vector representing the image feature in a specific direction. For example, the image obtained by rotating the center vector A by 45 degrees is: , then the direction vector is .

[0063] In this embodiment, the covariance matrix is ​​a matrix used to describe the relationship between multi-dimensional random variables, as follows: ; Every element It represents the covariance between the corresponding direction vector and the center vector, reflecting the linear relationship between them.

[0064] In this embodiment, the mixed feature vector is generated by the covariance matrix and is used to represent the mixing of image features in different directions. For example, the mixed feature vector obtained by the above covariance matrix A is , is Each element is obtained by eigenvalue extraction. Represents the mixed features of the corresponding direction feature vector and the center vector.

[0065] In this embodiment, projection refers to a process of mapping one vector onto another vector.

[0066] In this embodiment, the projection coefficient represents the degree of mapping of the mixed feature vector on each directional feature image. The mixed eigenvector of is projected onto the kth direction vector The final projection coefficient is .

[0067] In this embodiment, the weight coefficient is a coefficient representing the contribution of the mixed feature vector to each directional feature image.

[0068] The working principle and beneficial effects of the above technical solution are: through multi-angle feature extraction and weight adjustment, the expression ability and classification performance of image features are improved, and the accuracy of system text recognition is enhanced.

[0069] Embodiment 5: Based on the above embodiment 4, the intelligent identification module further includes: Fusion unit: performing feature fusion on 8 directional vectors based on the weight coefficient of the directional feature image to obtain a fused feature vector, wherein the fused feature vector includes a plurality of element features, and each element feature includes a horizontal sub-feature and a vertical sub-feature; Matching unit: matching the fused feature vector based on the feature-text library to obtain the text feature corresponding to each element feature in the fused feature vector; Calculate the initial similarity between each element feature and the corresponding matching text feature respectively; ; in, represents the initial similarity between the i-th element feature in the fused feature vector and the corresponding matching text feature; Represents the horizontal gradient difference between the i-th element feature in the fused feature vector and the corresponding text feature. Represents the vertical gradient difference between the i-th element feature in the fused feature vector and the corresponding text feature. ) represents the tangent function, and Respectively represent the i-th element feature and the corresponding text feature in the fused feature vector, Indicates based on The dot product function of express Norm ratio; Correcting the initial similarity to obtain a final similarity of the corresponding element features; ; in, Represents the final similarity of the i-th element feature in the fused feature vector, ) represents the exponential function, Indicates the maximum matching similarity of all element features in the fused feature vector. Indicates the minimum matching similarity of all element features in the fused feature vector. Represents the mean similarity of all element features in the fused feature vector; Indicates that based on all The variance of The mean of the variances of Based on the final similarity of each element feature in the fused feature vector, a corresponding text conversion scheme is matched from a similarity-text conversion mapping table to convert the corresponding image text content into original text data.

[0070] In this embodiment, feature fusion refers to combining feature information from different directions to form a comprehensive feature representation. For example, the feature of vector 1 is , the characteristics of vector 2 are , then their fusion vector is: ,in is the weight coefficient of vector 1, is the weight coefficient of vector 2.

[0071] In this embodiment, the fused feature vector is a result generated in the feature fusion process, and is a collection of feature information extracted from multiple direction vectors.

[0072] In this embodiment, the element feature refers to each part in the fused feature vector, and these parts represent different information in the image.

[0073] In this embodiment, the horizontal sub-feature refers to the horizontal component of each element feature in the fused feature vector.

[0074] In this embodiment, the vertical sub-feature refers to the vertical component of each element feature in the fused feature vector.

[0075] In this embodiment, the feature-text library is a database storing the correspondence between features and texts.

[0076] In this embodiment, the text feature is text information used to describe the image content, including text descriptions of the image content, keywords, classification labels, etc.

[0077] In this embodiment, the initial similarity refers to the initial similarity between each element feature and the corresponding text feature calculated in the matching unit.

[0078] In this embodiment, the horizontal gradient difference refers to the difference between the horizontal gradient of the i-th element feature in the fused feature vector and the corresponding text feature.

[0079] In this embodiment, the vertical gradient difference is the difference between the vertical gradient of the i-th element feature in the fused feature vector and the corresponding text feature.

[0080] In this embodiment, the dot product refers to the result obtained by multiplying the elements at corresponding positions in two vectors and then adding them together. For example, the dot product of A and B is:

[0081] In this embodiment, the norm refers to a measure of the size of a vector, which is the square root of the sum of the squares of all elements in the vector.

[0082] In this embodiment, the final similarity refers to the final similarity between each element feature and the corresponding matching text feature calculated based on the modified initial similarity.

[0083] In this embodiment, the matching similarity refers to the similarity between the element feature and the corresponding matching text feature.

[0084] In this embodiment, the similarity-text conversion mapping table is a table that maps similarity values ​​to corresponding text conversion solutions.

[0085] In this embodiment, the text conversion scheme is obtained according to the similarity-text conversion mapping table, and the content types include: mapping feature similarity to corresponding text content, identifier, etc. according to predefined rules.

[0086] The working principle and beneficial effects of the above technical solution are: using directional feature images to fuse feature vectors, achieving image-to-text conversion through similarity calculation and correction, thereby improving matching accuracy and data conversion efficiency.

[0087] Embodiment 6: Based on the above embodiment 1, the text processing module includes: Cleaning unit: traverses the original text data, marks unrecognized words in the original text data as target words, and compares the target words with the standard dictionary; If there is a standard similar word in the target word, the target word is converted into a standard similar word with the highest similarity; otherwise, the target word is determined to be an unknown word, and a deletion operation is performed, and the first contract data is output after traversal; Other format units: filter and convert the unstructured data in the original text data in combination with the preset target text format, and output the second contract data; A text proofreading unit: based on a proofreader, corrects misspelled words in the first contract data and the second contract data to obtain standard text data.

[0088] In this embodiment, unrecognized vocabulary refers to vocabulary for which no matching item can be found in the vocabulary library during text processing.

[0089] In this embodiment, the target vocabulary refers to the vocabulary marked as unrecognized in the original text data.

[0090] In this embodiment, the standard dictionary refers to a dictionary that contains a vocabulary of terms in the contract field and their corresponding definitions or explanations.

[0091] In this embodiment, vocabulary comparison refers to the process of comparing and matching the vocabulary in the original text data with the vocabulary in the standard dictionary.

[0092] In this embodiment, the standard similar vocabulary refers to known vocabulary similar to the target vocabulary found in a standard dictionary.

[0093] In this embodiment, unknown words refer to words for which no similar words can be found in a standard dictionary when processing text data.

[0094] In this embodiment, the first contract data refers to the data processed by the vocabulary cleaning unit.

[0095] In this embodiment, unstructured data refers to data that does not have a clearly defined format or pattern in the text, and the types include: free text, unordered lists, free-format tables, etc.

[0096] In this embodiment, the preset target text format refers to the text format structure defined when processing unstructured data, including the layout of the document, the arrangement of fields, format requirements of specific data, etc.

[0097] In this embodiment, filtering refers to removing the portion that does not conform to the preset target text format from the original text data.

[0098] In this embodiment, the transformation converts the unstructured data into data that is recognizable and conforms to a preset target text format.

[0099] In this embodiment, the second contract data refers to text data obtained by processing the unstructured data.

[0100] The working principle and beneficial effects of the above technical solution are: through the cleaning unit to identify and convert unrecognized words, other format units to convert unstructured data, and the text proofreading unit to correct spelling errors, automated contract text cleaning and format unification are achieved, thereby improving text accuracy and readability.

[0101] Embodiment 7: Based on the above embodiment 1, the text analysis module includes: Word segmentation unit: used to segment standard text data into word sequences, and filter out useless stop words based on a general word dictionary to obtain a standard word sequence; Entity recognition unit: Based on the contract entity library, the named entity recognition system is used to identify entities of standard word sequences to obtain entity sequences; Keyword extraction unit: based on the contract keyword recognition library, extract keywords from the standard word sequence to obtain a keyword sequence; Judgment unit: Take the union of the entity sequence and the keyword sequence, filter out the useless words in the union, and obtain the key contract data.

[0102] In this embodiment, the word sequence refers to a sequence of words obtained by segmenting the text according to spaces or other symbols.

[0103] In this embodiment, the general word dictionary refers to a dictionary containing common words.

[0104] In this embodiment, useless stop words refer to words that lack practical meaning or carry little information in the context of text processing tasks, such as "is", "of", "and", etc.

[0105] In this embodiment, the standard word sequence refers to a word sequence in text data obtained after word segmentation and filtering.

[0106] In this embodiment, the contract entity library refers to a database containing contract-related entities, including: specific information such as names of people, organization names, dates, locations, etc. in the contract.

[0107] In this embodiment, the entity sequence refers to a word sequence in the text that is identified as having a specific entity type.

[0108] In this embodiment, the contract keyword recognition library is a database containing contract-related keywords, including: contract type, terms, amount, date, responsibility, etc.

[0109] In this embodiment, the keyword sequence refers to a sequence of words that are identified as having importance in the text.

[0110] The working principle and beneficial effects of the above technical solution are: through word segmentation, entity recognition and keyword extraction, useless words are filtered out, the accuracy and efficiency of contract data processing are improved, and more accurate key contract data are obtained.

[0111] Embodiment 8: Based on the above embodiment 1, the text analysis module includes: Syntax extraction unit: performs grammatical analysis on key contract data, determines the relationship and hierarchy between terms, and adds grammatical terms in a grammatical logical order to generate contract terms; Structured data unit: standardize the contract terms according to the format of the standard preset contract structure to obtain the formatted contract data of the target contract.

[0112] In this embodiment, grammatical analysis is a technology for processing natural language, which determines the relationship and hierarchical structure between words by analyzing the structure and grammatical rules of sentences.

[0113] In this embodiment, the hierarchical structure refers to the organizational structure between contract clauses, that is, the relationship and arrangement order between different clauses. For example, the main clauses, sub-clauses, additional clauses, etc. in the contract constitute the hierarchical structure of the contract.

[0114] In this embodiment, the grammatical logical order refers to the order of organizing text data according to grammatical rules and logical order.

[0115] In this embodiment, the contract terms refer to specific clauses or items stipulated in the contract, including: payment terms, delivery terms, confidentiality terms, dispute resolution terms, etc.

[0116] In this embodiment, the standard preset contract standard structure refers to a contract structure and format developed according to a specific industry, legal requirements or practices.

[0117] In this embodiment, the modification and standardization process refers to the process of modifying and standardizing the contract terms. Modification is to make necessary modifications and amendments to the contract terms based on the results of grammatical analysis and grammatical logical order; standardization is to adjust the contract terms according to the preset contract standard structure.

[0118] The working principle and beneficial effects of the above technical solution are: automatic extraction, analysis and formatting of contract terms are achieved through natural language processing, which improves the efficiency and accuracy of contract processing and enhances the efficiency of contract management.

[0119] Embodiment 9: A method for digital intelligent identification of contracts, such as Figure 2 As shown, including: Step 1: Use an image acquisition device to acquire an initial image of the target contract, and pre-process the initial image to obtain a standard image in the electronic copy format; Step 2: Use OCR tools to recognize the image text content of the standard image and convert the image text content into original text data; Step 3: Perform text processing on the original text data to obtain the standard text data of the target contract; Step 4: Use natural language recognition technology to analyze standard text data, extract key contract data, and integrate the key contract data into formatted contract data with a standard structure.

[0120] The working principle and beneficial effects of the above technical solution are: using image acquisition equipment to acquire contract images, using OCR tools to recognize text, and then performing text processing and analysis to convert contract data into structured formatted data, thereby improving the efficiency and accuracy of contract text recognition and realizing the automation and intelligence of contract management.

[0121] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A digital intelligent contract recognition system, characterized in that: include: Data acquisition module: using image acquisition equipment to acquire the initial image of the target contract, and pre-processing the initial image to obtain a standard image in the electronic copy format; Intelligent recognition module: Use OCR tools to recognize the image text content of standard images and convert the image text content into original text data; Text processing module: performs text processing on the original text data to obtain the standard text data of the target contract; Text analysis module: Use natural language recognition technology to analyze standard text data, extract key contract data, and integrate the key contract data into formatted contract data with a standard structure.

2. A digital intelligent contract identification system according to claim 1, characterized in that: The data acquisition module comprises: Equipment configuration unit: Determine the type of image acquisition equipment based on the standard image requirements of the recognition contract, select a standard shooting environment based on the surrounding environment parameters acquired by the sensor, and perform installation and configuration based on the installation manual of the image acquisition equipment; Image acquisition unit: used to acquire the initial image of the target contract using the installed image acquisition device.

3. A digital intelligent contract identification system according to claim 1, characterized in that: The data acquisition module comprises: A preprocessing unit: performing image enhancement on the initial image to obtain a clear image in an electronic copy format, and adjusting the clear image according to a preset standard ratio to obtain a first image; Correction unit: performs horizontal correction processing on the text part of the first image, and calls a blank area recognition algorithm to crop the area to be cropped, so as to obtain a second image; Compression unit: compresses the second image to a standard image size using a compression tool to obtain a standard image of the target contract.

4. A digital intelligent contract identification system according to claim 1, characterized in that: The intelligent identification module comprises: The first processing unit is used to extract the features of the standard image by using an OCR tool after the standard image is loaded to generate a first feature image. The second processing unit: rotates the first feature image at 8 angles to obtain directional feature images at 8 angles, and extracts features from the 8 directional feature images to obtain 8 directional feature values; Vector acquisition unit: converting each directional feature value into a corresponding directional vector according to a vector conversion standard, and taking the feature vector of the first feature image as a center vector; Matrix construction unit: constructs a covariance matrix based on all direction vectors and the center vector; Projection unit: obtains a mixed eigenvector according to the covariance matrix, projects the mixed eigenvector onto each direction vector to obtain a projection coefficient, and uses the corresponding projection coefficient as a weight coefficient of the corresponding directional feature image.

5. A digital intelligent contract identification system according to claim 4, characterized in that: The intelligent identification module further includes: Fusion unit: performing feature fusion on 8 directional vectors based on the weight coefficient of the directional feature image to obtain a fused feature vector, wherein the fused feature vector includes a plurality of element features, and each element feature includes a horizontal sub-feature and a vertical sub-feature; Matching unit: matching the fused feature vector based on the feature-text library to obtain the text feature corresponding to each element feature in the fused feature vector; Calculate the initial similarity between each element feature and the corresponding matching text feature respectively; ;in, represents the initial similarity between the i-th element feature in the fused feature vector and the corresponding matching text feature; Represents the horizontal gradient difference between the i-th element feature in the fused feature vector and the corresponding text feature. Represents the vertical gradient difference between the i-th element feature in the fused feature vector and the corresponding text feature. ) represents the tangent function, and Respectively represent the i-th element feature and the corresponding text feature in the fused feature vector, Indicates based on The dot product function of express Norm ratio; Correcting the initial similarity to obtain a final similarity of the corresponding element features; ;in, Represents the final similarity of the i-th element feature in the fused feature vector, ) represents the exponential function, Indicates the maximum matching similarity of all element features in the fused feature vector. Indicates the minimum matching similarity of all element features in the fused feature vector. Represents the mean similarity of all element features in the fused feature vector; Indicates that based on all The variance of The mean of the variances of Based on the final similarity of each element feature in the fused feature vector, a corresponding text conversion scheme is matched from a similarity-text conversion mapping table to convert the corresponding image text content into original text data.

6. A digital intelligent contract identification system according to claim 1, characterized in that: The text processing module comprises: Cleaning unit: traverses the original text data, marks unrecognized words in the original text data as target words, and compares the target words with the standard dictionary; If there is a standard similar word in the target word, the target word is converted into a standard similar word with the highest similarity; otherwise, the target word is determined to be an unknown word, and a deletion operation is performed, and the first contract data is output after traversal; Other format units: filter and convert the unstructured data in the original text data in combination with the preset target text format, and output the second contract data; A text proofreading unit: based on a proofreader, corrects misspelled words in the first contract data and the second contract data to obtain standard text data.

7. A digital intelligent contract identification system according to claim 1, characterized in that: The text analysis module comprises: Word segmentation unit: used to segment standard text data into word sequences, and filter out useless stop words based on a common word dictionary to obtain a standard word sequence; Entity recognition unit: Based on the contract entity library, the named entity recognition system is used to identify entities of standard word sequences to obtain entity sequences; Keyword extraction unit: based on the contract keyword recognition library, extract keywords from the standard word sequence to obtain a keyword sequence; Judgment unit: Take the union of the entity sequence and the keyword sequence, filter out the useless words in the union, and obtain the key contract data.

8. A digital intelligent contract identification system according to claim 1, characterized in that: The text analysis module comprises: Syntax extraction unit: performs grammatical analysis on key contract data, determines the relationship and hierarchy between terms, and adds grammatical terms in a grammatical logical order to generate contract terms; Structured data unit: standardize the contract terms according to the format of the standard preset contract structure to obtain the formatted contract data of the target contract.

9. A method for digital intelligent contract identification, characterized in that: include: Step 1: Use an image acquisition device to acquire an initial image of the target contract, and pre-process the initial image to obtain a standard image in the electronic copy format; Step 2: Use OCR tools to recognize the image text content of the standard image and convert the image text content into original text data; Step 3: Perform text processing on the original text data to obtain the standard text data of the target contract; Step 4: Use natural language recognition technology to analyze standard text data, extract key contract data, and integrate the key contract data into formatted contract data with a standard structure.

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