211 results about "Document recognition" patented technology

The present disclosure relates to the technical field of document recognition, and provides an intelligent document recognition method and apparatus, an electronic device and a storage medium. The method comprises: extracting text information and layout information from a document to be recognized; acquiring a template containing a specified field; on the basis of an industry knowledge base corresponding to the document, adding a label to the text information; and, on the basis of the layout information and the label, using a large language model to recognize text information matched with the specified field from the text information to which the label has been added, the large language model being a large language model that has been trained by using the industry knowledge base. In the embodiments of the present disclosure, document recognition results more conform to industry attributes, improving document recognition accuracy.

The invention belongs to the technical field of artificial intelligence, and relates to a multi-modal large model-based document identification method, system and device and a medium, and the method comprises the following steps: 1) image preprocessing: preprocessing a document image input by a user; 2) multi-modal large model reasoning: based on the pre-processed document image, the configured JSON template and the cue word template, performing reasoning by a multi-modal large model to obtain a JSON result; 3) OCR identification: identifying the document image input by the user by using an OCR identification technology to obtain an OCR identification result; and 4) verification: performing similarity comparison on the OCR recognition result and the JSON result, and determining a document recognition result based on a similarity comparison result. The method is high in generalization, can adapt to various types of receipts, and can provide an efficient and accurate recognition result.

The invention discloses an OCR (Optical Character Recognition)-based document automatic recognition intelligent management system, and relates to the technical field of intelligent document OCR processing. A preprocessing module adopts adaptive filtering and a GAN (Generic Area Network) to repair low-quality documents and separate seals from characters; the recognition engine realizes end-to-end recognition of inclined characters and mixed characters through a multi-scale feature pyramid network, and the semantic module constructs a dynamic knowledge graph and supports event evolution reasoning and cross-year correlation analysis; the classification module realizes small sample classification based on a prototype network, the security module realizes fine-grained authority control and anomaly detection through a block chain and federated learning, the text edge definition is improved after system processing, and real-time reasoning is supported. The intelligent level of document processing is improved through cooperation of multiple modules, the preprocessing and recognition module solves the complex scene problem, and deep analysis and correlation analysis are achieved through semantic understanding and a knowledge graph; the security module guarantees data security; and the efficiency and decision scientificity are comprehensively improved.

Techniques are described herein for generating a semantically related search query that differs by at least one lexical token from document data provided as input. A machine-learning model may be trained using supervised and / or deep learning algorithms and a training data set including historical queries and the document data identified as being semantically related to those queries. The semantically-related search query may be associated with the document and utilized in subsequent searches to match the document to a subsequent query based on identifying a lexical match between the subsequent search query and the semantically-related search query associated with the document. These techniques enable semantically related search queries to be assigned offline but utilized to identify semantically related documents using lexical matching techniques. Identifying these types of matches create more diverse search results while maintaining a high degree of relevance between the query and the documents of the result set.

The invention discloses a digital reading system based on a knowledge graph and a text image recognition technology. The system comprises a collection module used for collecting financial data and a first financial document from a plurality of financial data sources; the construction module is used for identifying a target entity in the financial field in the financial data and constructing a graph structure of a financial knowledge graph; the first analysis module is used for receiving a second financial document uploaded by the user, identifying a target keyword in the second financial document and generating a mind map corresponding to the target keyword according to a preset hierarchical structure; the second analysis module is used for identifying document elements in the first financial document; the typesetting module is used for acquiring screen information of the mobile terminal equipment; the watermark adding module is used for generating a digital watermark according to the copyright information and the custom information; and the question processing module is used for receiving the query question. The technical problem that financial knowledge processing efficiency of a related digital reading system is low is solved.

The invention discloses an unstructured file identification method based on primitive identification, and the method comprises the following steps: (1) preprocessing: cutting and zooming an electrical wiring drawing to a required size, and carrying out the gray processing; (2) primitive recognition: constructing a feature extraction network of an electrical primitive detection model by adopting a YOLO electrical primitive detection algorithm fused with an attention mechanism; (3) character recognition, which comprises the following steps: removing primitives, detecting an annotated text area, constructing a text candidate box, segmenting and adjusting an annotated text boundary, recognizing annotated text content, and filtering an electrical text recognition result; and (4) electrical element association relationship analysis, which comprises the following steps: frame element extraction, primitive group region division, template matching and electrical primitive-annotation text association. According to the method, the primitives and characters of the electrical wiring diagram can be obtained, the problem that different fonts and styles appear in marked characters can be solved, and semantic information can be understood.

The invention provides an enterprise intelligent document identification and automatic classification archiving management processing method and system, and relates to the technical field of intelligent document processing, and the method comprises the steps: constructing a hierarchical cognitive attention network, extracting initial features of a document through a capsule network, and carrying out semantic processing through a memory enhancement neural network; and adaptively adjusting feature extraction parameters based on document complexity to obtain cognitive feature representation. Mapping the cognitive feature representation to a non-Euclidean manifold space, determining an optimal classification boundary by using an improved particle swarm optimization algorithm, and generating a multi-dimensional classification vector; and constructing an adaptive feedback optimization network, fusing the cognitive features and the classification vectors, carrying out iterative optimization based on the consistency evaluation score until a preset threshold value is reached, and outputting a final classification result.

The invention provides a document recognition method and device, computing equipment and a storage medium, and the method comprises the steps: inputting a to-be-recognized document into an optical character recognition (OCR) engine, and obtaining a first recognition result output by the OCR engine; respectively inputting the to-be-recognized document and the recognition prompt words of the different types of data into a plurality of different multi-modal large language models to obtain a plurality of second recognition results output by the plurality of different multi-modal large language models; aiming at the text data, aligning the first recognition result with the plurality of second recognition results to obtain a first output result; for the graph data, obtaining a second output result based on the plurality of second recognition results; and obtaining an identification result of the to-be-identified document based on the first output result and the second output result. According to the scheme, OCR identification can be accurately and effectively carried out on the document.

The invention discloses a knowledge question-answering method based on multiple agents and heterogeneous data sources, which comprises the following steps of: collecting an original document, identifying data, processing the format of the data, and respectively realizing the construction of a vector database and a graph database; obtaining a standard question and answer data set, and generating expansion questions by utilizing partial sub-graphs of the knowledge graph to complete construction of a basic question pool; obtaining user questions, and performing preliminary retrieval in the basic question pool; for problems needing deep retrieval, according to the types and characteristics of the problems, the routing agent flexibly calls required tools or distributes tasks to different retrieval agents; and the answer agent integrates the obtained retrieval results to generate a final answer. According to the method, a multi-agent cooperation mechanism is utilized, the intention of the user can be accurately recognized, dynamic information retrieval is carried out in different types of data sources, and meanwhile efficient and rapid intelligent question and answer services are provided by constructing the basic question pool.

The invention belongs to the field of bill recognition, and provides a bill recognition method, system and equipment based on a large model, and the method comprises the steps: obtaining bill image data containing text information, and carrying out the preprocessing of the obtained bill image data; evaluating image quality, language distribution and layout complexity of the preprocessed image, and determining a processing path of document image data by integrating evaluation results; according to the determined processing path, calling a corresponding optical character recognition model to extract text information in the document image data; performing deep semantic analysis on the extracted text data by using a pre-trained deep learning large model, and extracting key feature information; and performing rule compliance analysis on the text information or the extracted key feature information, and generating an identification analysis report according to an analysis result. According to the invention, the processing speed and accuracy are improved, the adaptability to various document formats is enhanced, and the limitation in the prior art is effectively solved.

The invention discloses a training method, device and equipment for a large intelligent recognition model of archive files and a medium, and relates to the technical field of document recognition. The training method comprises the following steps: constructing a self-supervised diffusion model for first-stage training: carrying out random mask processing on image samples to generate mask image samples, respectively inputting the mask image samples into an image encoder to extract high-dimensional information, and further enhancing the discrimination of an attention map by using a token selection module, the weight of task related parameters is dynamically adjusted through an attention refocusing mechanism, the perceptual ability of the model to a task target is improved, a text encoder embedded by an empty text is combined to serve as condition input of a diffusion model, and the encoder is optimized by using generation feedback of the diffusion model; and constructing a second-stage fine-tuning Qwen-vl large model: freezing the image encoder trained in the first stage, and finely tuning the Qwen-vl large model by using a small number of samples. According to the method, the visual reasoning and fine-grained sensing capabilities of a large archive identification model in a complex scene are realized, and the generalization and precision of archive identification are improved.

The invention relates to an AD-RAG-based vehicle after-sales maintenance document analysis and maintenance knowledge acquisition system and method, and the system comprises a document analysis module which carries out the layout analysis and document recognition of after-sales maintenance document information, and obtains structural data; the knowledge organization module is used for establishing an association relationship between the text data and the structural relationship in the structured data to obtain a maintenance knowledge graph; the knowledge retrieval enhancement module is used for analyzing and expanding the vehicle fault query request based on an AD-RAG model to obtain an expanded query request, and querying in a maintenance knowledge graph based on the expanded query request to obtain document fragment information; and the task decomposition and reasoning optimization module analyzes and decomposes the extended query request to obtain at least one fault sub-problem sequence, and performs fault reasoning through a preset fault reasoning model based on the fault sub-problem sequence and the document fragment information to obtain a fault maintenance suggestion result. The retrieval requirement can be better met, and the obtaining efficiency of the maintenance knowledge can be improved.

The invention relates to the field of customs document text feature recognition, in particular to a deep learning-based customs document text feature recognition method, which comprises the following steps of: preprocessing a real-time customs document text to obtain customs document text features; establishing a customs document text classification analysis model based on deep learning according to the customs document text features; and performing feedback adjustment processing by using the customs document text classification analysis model to obtain a customs document text feature recognition result, and establishing a dynamic threshold judgment system by considering multi-dimensional features such as data types, data capacity, text content, timestamps and historical data at the same time, so that the abnormal document recognition accuracy is improved, and the user experience is improved. Meanwhile, a cross validation mechanism of the data content classification model and the data content analysis model reduces the omission ratio of high-risk receipts in the test, and effectively solves the problem of cold start of training data.

The invention relates to the cross technical field of computer vision and natural language processing, and particularly provides a zero sample template inference and document structured recognition method and device, and the method comprises the following steps: S1, document image collection and preprocessing; s2, performing layout sensing partitioning and position coding; s3, priori or example information is constructed and injected; s4, performing cross-modal fusion and expression construction; s5, performing automatic format analysis and field slot filling; s6, generating a cue word-free extraction instruction; s7, performing field area parallel character recognition; s8, performing semantic verification and result standardization; and S9, outputting the structured field-value data. Compared with the prior art, the method has the advantages that the dependence of a traditional method on template making, cue word writing and large-scale sample training can be avoided, the flexibility, accuracy and online speed of document structured recognition are remarkably improved, and the method has good intelligence and rapid adaptation capacity and is suitable for diversified document recognition scenes.

The invention discloses an OCR-based medical document intelligent identification method and system, and relates to the technical field of document identification, and the method comprises the steps: collecting a medical document image through a mobile terminal, generating a binary image based on the collected image through U-Net in combination with multi-scale feature fusion and an attention mechanism, and cutting the binary image; based on the cut binary image, text information is extracted through an OCR model, and a structured field is extracted according to the typesetting rule and geometric distribution of the medical document; and performing deterministic rule judgment and risk assessment on the structured data. According to the method, the image calculation complexity is reduced through standard graying processing, the text region feature extraction precision is improved by fusing a U-Net structure of a CBAM attention mechanism, effective fusion and noise suppression of multi-scale features are realized in combination with Attention Gate, text direction correction is realized in combination with Hough transform, and the text detection robustness and recognition accuracy are improved.

The embodiment of the invention relates to the technical field of information processing, and discloses a document recognition method, device and equipment and a storage medium, and the method comprises the steps: obtaining a to-be-recognized text image and a cue word which is used for prompting to-be-recognized information in the to-be-recognized text image; recognizing the to-be-recognized text image based on a preset orientation recognition algorithm to obtain a to-be-adjusted angle; rotating the to-be-recognized text image based on the to-be-adjusted angle to obtain a target text image; and inputting the target text image and the cue word into the multi-modal large model to obtain a first text. The orientation of the target text image input to the multi-modal large model is correct, so that the high learning ability of the multi-modal large model is fully utilized to perform document recognition. The problem that error superposition is easy to occur when the OCR is used for recognizing the document image is avoided, and the problem that the accuracy is low when a multi-mode large model is used for recognizing the document image with the wrong orientation is also avoided. And the accuracy of document identification is improved.

The invention discloses a document recognition method, an intelligent interaction method, a related device, equipment and a medium, and the document recognition method comprises the steps: carrying out the recognition based on a to-be-recognized document, and obtaining a layout element in the to-be-recognized document and a recognition result of the layout element; analyzing based on the identification result of the title to obtain a first title sequence; correcting the title level of the first title sequence to obtain a second title sequence; performing verification based on the second title sequence to obtain a verification result; wherein the verification result represents whether the second title sequence is correct or not; and in response to the verification result representing that the second title sequence is wrong, selecting the second title sequence as a new first title sequence, and returning to the step of correcting the title level of the first title sequence and obtaining the second title sequence for iteration until an end condition is met. According to the scheme, the continuity of title modeling during document recognition can be improved, so that the hierarchical relation of all titles in the document can be distinguished, especially in a cross-page scene.

The invention relates to the technical field of document recognition and intelligent analysis, and provides an intelligent paper analysis system based on multi-modal content extraction and a large language model. The system comprises a file uploading module, a PDF and LaTeX analysis module, a user interaction module, a paper analysis module and a data storage management module which are connected in sequence. The file uploading module is used for receiving an academic paper file uploaded by a user; the PDF and LaTeX analysis module is responsible for carrying out file analysis, information extraction and formatting processing on the uploaded academic papers; the user interaction module is used for realizing efficient interaction between the system and a user, so that the user can quickly obtain required academic information and service; the paper analysis module is used for automatically extracting key information in a paper and generating a structured abstract based on a pre-trained large language model in combination with a natural language processing technology; and the data storage management module is used for storing, managing and maintaining system data. Through cooperative work of multiple modules, the academic papers can be intelligently analyzed and summarized efficiently and accurately, and the automation level of papers processing and the user experience are remarkably improved.

Identifying documents that contain potential code words using a machine learning model. In some embodiments, a method may include receiving documents, identifying a first corpus and a second corpus in the documents, extracting a first set of word embeddings from the first corpus and a second set of word embeddings from the second corpus, generating a first vector space for the first set of word embeddings and a second vector space for the second set of word embeddings using a machine learning model, performing a vector rotation to improve alignment of the first set of word embeddings with the second set of word embeddings, identifying a word embedding in the first vector space that is not aligned with a corresponding word embedding in the second vector space as a potential code word, and identifying one or more documents that contain the potential code word in the first corpus.

The present invention relates to the field of document recognition technology, and discloses a method and system for automatically identifying regulations associated with letters. The method comprises the steps of file uploading, preprocessing, text recognition, chapter recognition, content extraction, tree structure building, and result display. In the image preprocessing, the present invention has accurate grayscale, flexible noise reduction, adaptive binarization, and efficient tilt correction, thereby improving image quality and ensuring the basis for text recognition. The text recognition process is innovative, and line segmentation and character segmentation are reliable and accurate. Character recognition combined with a standard character template library is intelligent and efficient, thereby improving accuracy. Table of contents recognition is judged in multiple dimensions through regular expressions, font features, and numbering structures, and the hierarchy is scientifically and verified, and the content extraction is reasonable. The tree structure is built based on the hierarchy, combined with refined node and content pairs, with clear levels. The modules of the system work together to achieve automated processing, providing users with convenient and intuitive display of regulatory information.

The invention relates to the field of image recognition, discloses an image recognition method and an image recognition system, and systematically solves the core pain point of traditional document recognition through optical-topology fusion processing and a dynamic resource allocation mechanism. The image recognition method is composed of an acquisition module, a grid module, a phase module, a setting module and a distribution module, pixel brightness is calculated and coded based on document RGB data, and a two-dimensional coding matrix is generated; constructing a geometric correction grid, forming a nonlinear constraint field, and enhancing the anti-deformation capability of the image; generating spiral phase light waves in the constraint field by using a spatial light modulator, and generating a time-varying phase map; determining a local topology index by detecting the number of phase jump times; and extracting the closed boundary region as a character block, and generating an analysis result file. The system breaks through traditional limitation, improves image geometric correction precision, feature extraction sensitivity and boundary judgment accuracy, efficiently completes document analysis, and is suitable for scenes such as document digitization and information retrieval.

The invention provides a table document recognition method and device, electronic equipment and a storage medium, and the method comprises the steps: carrying out the table detection of a to-be-recognized document image, and obtaining a table image; performing table line detection on the table image to obtain a transverse line segment and a vertical line segment; determining table cell intersection point information based on the transverse line segments and the vertical line segments, and determining table cell structure information according to the intersection point information; performing text detection on the to-be-recognized document image to obtain a detection result; and matching each text detection box in the detection result with each cell in the table cell structure information to obtain a text line region corresponding to each cell, and performing text recognition on the text line region to obtain a text recognition result of each cell. According to the table structure identification method and device, table line detection is carried out on the table image, the transverse line segments and the vertical line segments are obtained according to detection, the intersection point information and the structure information of the table cells can be accurately determined, and the accuracy of table structure identification is improved.

The invention relates to the technical field of natural language processing, in particular to a verifiable large model retrieval enhancement generation system and method based on an evidence chain, and the method comprises the steps: receiving an initial query, recognizing the fuzziness and information gap of the initial query in combination with an associated retrieval document, and generating a supplementary query set; based on the initial query, the supplementary query and the corresponding retrieval document, generating candidate answers with references and verifying the information supportability of the candidate answers; for the candidate answers passing the verification, extracting support information and constructing a hierarchical attribution mapping relation; and integrating the information to form to-be-evaluated information, and if the current verified to-be-evaluated information meets a preset sufficiency condition, integrating the generated preliminary answer and the to-be-evaluated information to synthesize a target answer. The method effectively overcomes the defects that a traditional RAG system is fragmented in information integration, has one-sided fuzzy query and answer, is low in attribution efficiency and excessively depends on retrieval content, and has the advantages of answer comprehensiveness, verifiability and deployment lightweighting.

This invention relates to the field of image recognition technology, and more particularly to a general-purpose structured document recognition method and system based on deep learning. The method includes: acquiring document image information and preprocessing the acquired images; inputting a standardized document image into a text detection network to locate text instances in the image and saving the location information to a file; extracting text images from the document image based on the detected text instance location coordinates and inputting them into a text recognition network for recognition, saving the recognition result after the location information; using a key text extraction network to classify the text entities in the recognition result, removing non-key types, and then saving the classification result after the recognition result; and performing structured processing on the key text extraction result and displaying it. This invention can extract key text information from complex document images, enabling intelligent document reading, and is applicable to various types of documents.

The invention relates to a document recognition method and device, electronic equipment and a storage medium. The method comprises the steps that a target document and title information of the target document are acquired; performing hierarchical analysis on the title information, and establishing a directed acyclic graph corresponding to the target document according to a hierarchical analysis result; based on the directed acyclic graph, a tree diagram corresponding to the target document is constructed, and each node of the tree diagram correspondingly records a document title of the target document; and according to the corresponding relationship between each node in the tree diagram and the document title, obtaining the document content of the target document by using the tree node of the tree diagram as an index. According to the method, the tree diagram corresponding to the document is constructed according to the support relationship, and the clear hierarchical structure is analyzed through the tree diagram, so that a document management system with the hierarchical structure is formed, the analysis requirements of different types of documents are met, and the legal content retrieval and analysis efficiency is remarkably improved.

The invention discloses a document data entry method, electronic equipment, a storage medium and a program product, and relates to the technical field of document recognition, and the document data entry method comprises the following steps: obtaining a to-be-recognized document containing at least one to-be-recognized page; the document to be recognized is recognized through the optical character recognition technology, a first recognition result and a target page are obtained, and the target page is a page to be recognized containing non-text elements; identifying the target page through the target large language model to obtain a second identification result; and inputting the first recognition result and the second recognition result into the target form according to the similarity between the form field of the preset target form and the first recognition result and the second recognition result, and obtaining the input target form. Through cooperative work of the OCR and the large language model, synchronous and efficient recognition of text and non-text information is achieved, and the accuracy and the automation level of document recognition and document data entry are improved in combination with an intelligent matching entry mechanism.

The invention provides a wired table recognition method based on morphology, and the method comprises the following steps: S1, carrying out the graying processing of all read-in to-be-detected three-channel images, obtaining a gray image, carrying out the binarization processing of the obtained gray image, and obtaining a binary image of an original image; and S2, carrying out corrosion and expansion operation on the binary image by using a detection core, and forming an Excel format file by identifying a text table format and the like in the image so as to solve the problem of a special-shaped table. The problem that in the prior art, table recognition is always a research difficulty in the field of document recognition is solved, and the method is high in running speed, high in stability and good in interpretability. And the method is innovative, and can be used for well processing conditions such as special-shaped tables, cross-page disconnection, text cross-line, text wireless frame package and the like. The method can be popularized, and has good compatibility for identification of different types of wired tables. The method can be deployed in a server, a personal pc (personal computer) and embedded equipment, and is high in practical applicability.

The invention provides a tax compliance intelligent inspection method and platform, and relates to an image processing technology, and the method comprises the steps: obtaining an electronic layout sequence corresponding to electronic data based on a document recognition model, determining a customized capping region of each document page in the electronic data according to the electronic layout sequence, obtaining the capping range of each customized capping region, and determining the customized capping region of each document page in the electronic data; the method comprises the steps of obtaining a capping range of each document page, determining a capping identification type according to the capping range, cutting an anti-counterfeiting identification based on the capping identification type and the capping range to obtain sub-anti-counterfeiting identification of each document page, updating the electronic data based on the sub-anti-counterfeiting identification to obtain archived data, and storing the archived data in the corresponding document page. And in response to compliance verification information of the management end, verifying the current anti-counterfeiting mark corresponding to the current archived data based on the mark verification model to obtain compliance detection data, and sending the compliance detection data to the management end. According to the method, the risk that the electronic perforation seal is forged or tampered can be reduced, and the authenticity and integrity of electronic data are ensured.

The invention discloses a complex document recognition method based on layout analysis and OCR (optical character recognition) and a medium. Obtaining a to-be-recognized document image in real time, and performing preprocessing operation through a preprocessing module to obtain a to-be-recognized standard document image; processing the to-be-identified standard document image through a layout analysis module, and obtaining at least one target area image, a target area image type and an area vision matrix feature in combination with a layout analysis strategy method; performing text analysis on each target area image through a multi-mode OCR engine module according to the type of each target area image to obtain each text semantic analysis result and an area semantic matrix feature; obtaining a current visual text fusion feature through a multi-modal attention fusion module; and generating and feeding back a complex document identification output result according to the current visual text fusion feature. The problem that the accuracy is poor due to the fact that irregular layouts cannot be processed is solved, and the accuracy and flexibility of complex layout recognition are improved.

The invention relates to the technical field of document processing, and discloses a document analysis inspection-free method and system, electronic equipment and a storage medium, and the method comprises the following steps: inputting a document, identifying layout elements, and adding element-level inspection-free marks to the layout elements meeting a preset credible condition; extracting text, image and path information, and recombining the text, image and path information into structured memory data according to a reading sequence; executing multi-source information fusion and rule matching, and adding a structure-level inspection-free mark to a chapter structure division result meeting a preset credible condition; attribute labeling: adding an attribute-level inspection-free mark to the text block meeting a preset credible condition; and executing post-processing operation, adding a processing-level inspection-free mark to a post-processing result meeting a preset credible condition, and storing the post-processing result as a standard structured document with the inspection-free mark. According to the method, identification errors caused by prejudice or limitation of a single model are avoided, the identification accuracy and the processing efficiency can be improved, manual intervention is reduced, and the automation level is improved.