176 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.

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 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 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.

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 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 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 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.

The invention relates to the technical field of data security, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a security processing method, device and equipment oriented to software development and a medium. Obtaining an architecture design document to identify potential safety hazards and generate design improvement suggestions; generating a code based on the business logic description and the design improvement suggestion, and completing security detection and repair to obtain a processed code and a code repair record; performing security test on the processed code and recording a test result; collecting data of exception identification, hidden danger identification, code detection and repair and security test to update the security knowledge base; and generating a security analysis report based on the demand exception list, the design improvement suggestion, the code repair record and the test result. According to the invention, through a security identification and restoration process from demand to test, early discovery of security problems, linkage processing and knowledge self-updating are realized.

The invention discloses a document recognition and structuring method based on a visual language large model. The method comprises the following steps that S1, an input access layer receives a PDF or image document, and a page preprocessing and rendering layer unifies the resolution ratio and geometric parameters and performs denoising; s2, the heterogeneous recognition layer calls multiple recognizers in parallel on the same page to generate candidate results; s3, the alignment and fusion layer completes spatial alignment and text consistency evaluation of the candidate segments in a unified coordinate system to form a single main result; according to the method, the heterogeneous recognizers are called in parallel, a comprehensive scoring mechanism of space alignment, text consistency and model reliability is combined, fusion judgment is carried out on multi-source candidate results, segmentation errors and recognition deviation of a single model in complex scenes of nesting tables, cross-column titles and scanning noise can be avoided, and the recognition accuracy of the multi-source candidate results is improved. Stable output can still be kept in a multi-template and high-noise environment, and semantic consistency of recognition results is improved.

The invention discloses a document identification method and device, equipment, a storage medium and a computer program product. The method comprises the steps of obtaining target document text information and task condition information; the target document text information is task type document text information; inputting the target document text information and the task condition information into a pre-trained document recognition model to obtain a document recognition result output by the document recognition model; the document recognition model is obtained by performing target fine tuning on a large language model, and the target fine tuning comprises low-rank adaptive processing and regularization processing.

The invention relates to the technical field of content extraction, in particular to a multi-modal hybrid document OCR (Optical Character Recognition) and structured extraction method, which comprises the following steps of: acquiring an image text region bounding box and classifying a style, extracting a font or stroke sequence to generate a character positioning structure, dividing paragraph and sentence groups to classify semantic fields, and calculating a field matching relationship to generate structural mapping. According to the method, logic mapping is constructed through character two-dimensional coordinate sorting and paragraph contours, complete reconstruction of a page structure is enhanced, semantic field categories are extracted by using syntactic density of sentence paragraph division and inter-paragraph features, the accuracy of field classification is improved, and the method has the advantages of being simple in structure, convenient to operate and high in practicability. A field mapping relation is established through Jaccard similarity and part-of-speech consistency analysis between a head word and a standard field keyword, a field path index and a structure node link are clarified, field semantic affiliation and structure position output are unified, and the document structure reduction degree and field extraction accuracy are improved.

The embodiment of the invention provides an integrated testing method and device and a storage medium. In the embodiment of the invention, the AI large model, the MCP protocol and the multiple test frameworks of the execution layer are fused, so that an integrated test process with the cooperation of multiple test types is realized; the AI large model analyzes the test requirement document, recognizes multiple test requirements, is responsible for generating multiple test cases from the multiple test requirements, further converts the generated multiple test cases into multiple types of test scripts, calls a corresponding test framework to execute the test script corresponding to each test type through the MCP service of the execution layer, and performs the test on the test scripts. An integrated process from test case generation and script conversion to a test process is realized, and collaborative execution of multiple types of test scripts is realized. In addition, the integrated test framework further comprises a scheduling layer which is used for responding to the output of the AI large model, calling the MCP service of the execution layer, unloading the function of scheduling and managing the MCP service of the execution layer from the AI large model, and improving the reasoning efficiency of the AI large model.

The invention provides a method and device for improving an RAG recall effect, and belongs to the technical field of computers, and the method comprises the following steps: file input and typesetting structure analysis: identifying a typesetting unit for an input file, extracting text content in the typesetting unit, and generating associated data of typesetting and content; constructing a two-dimensional relation graph: performing semantic segmentation based on the typesetting units, and extracting a logic relation of the typesetting units; constructing a two-dimensional relation graph of the semantic relation and the typesetting relation; and multi-dimensional information fusion recall: receiving user query and performing semantic analysis, recalling similar semantic slices from a semantic community and a typesetting community, executing double-graph cross validation, dynamically adjusting weights, and calculating and obtaining a final recall result. According to the method, the knowledge base construction mode of the RAG is optimized from the perspective of typesetting, the multi-dimensional relation between text semantics and typesetting logic is fused, information association in the knowledge base is more comprehensive, and the retrieval recall can be based on the semantic similarity and the typesetting logic at the same time, so that the recall effect is remarkably improved.

The embodiment of the invention discloses a building construction document intelligent identification method and device and electronic equipment. A specific embodiment of the method comprises the following steps: responding to received user question information uploaded by a user through a user terminal, and receiving a synchronously uploaded building construction document; performing user intention recognition on the user question information to generate user intention information; determining whether a temporary knowledge base needs to be called according to the user intention information; calling a temporary knowledge base corresponding to the building construction document in response to calling determination; performing document identification on the building construction document by utilizing the temporary knowledge base and the user intention information so as to generate a document identification result corresponding to the user question information; and sending the document identification result to the user terminal as a user question and answer result. According to the embodiment, a more accurate question and answer result can be provided for the user.

The application discloses an OCR-based medical document intelligent identification method and system, relates to the technical field of document identification, and comprises the following steps: collecting a medical document image through a mobile terminal, generating a binary image based on the collected image through a U-Net combined with multi-scale feature fusion and an attention mechanism, and performing clipping; based on the clipped binary image, extracting text information through an OCR model, and extracting structured fields according to the layout rules and geometric distribution of the medical document; and performing deterministic rule judgment and risk assessment on the structured data. The application reduces the image calculation complexity through standard gray scale processing, improves the text region feature extraction accuracy through the U-Net structure combined with the CBAM attention mechanism, realizes effective fusion of multi-scale features and noise suppression in combination with the Attention Gate, realizes text direction correction in combination with the Hough transform, and improves the text detection robustness and recognition accuracy.

The invention provides a PDF (Portable Document Format) document content identification method and device, equipment and a storage medium. The method comprises the following steps: acquiring an access link of an unanalyzed document; downloading the target PDF document from the object storage service according to the access link; when the content region corresponding to the page type in the target PDF document is a text region, dividing the content region into a text region, a table region and an image region according to the page type corresponding to each content region; and when the content region is a text region, extracting a native text character sequence from the text region, and performing similarity calculation on the native text character sequence to generate a semantic coherent paragraph text. According to the method, the sentences with similar semantics in the text region are automatically divided into the same text block based on the cosine similarity, so that the text content with coherent and complete semantics is analyzed from the document, the problem that text paragraphs are broken after document recognition is solved, and the semantic coherence of the document content is effectively improved.