2140 results about "Text recognition" patented technology

The invention relates to the technical field of computers, provides an automatic test script dynamic generation method and system based on multi-modal AI recognition, and is used for remarkably improving the coverage dimension and execution robustness of test cases while reducing the manual maintenance cost. The method comprises the following steps: acquiring target test interface data, extracting dynamic interaction characteristics in the test interface data, calling a trained target detection network to carry out element positioning processing on the test interface data, generating an interface element coordinate set, and generating a target test interface based on the interface element coordinate set. Calling a character recognition network to carry out multi-modal feature analysis on the interface element distribution information, and generating a feature fusion result containing character semantic features and icon classification features; the feature fusion result is input into a script generation model for dynamic path planning processing, automatic test script elements are output, and the automatic test script elements comprise logic control instructions generated based on the element operation sequence and coordinate self-correction parameters.

The present invention provides a screenshot content extraction method based on a multimodal large model, which is used to extract OTA hotel structured information, including the following steps: obtaining a screenshot image with hotel information; performing text recognition on the image through an optical character recognition model to obtain a text information box set; performing text area positioning on the image through an image positioning model, and extracting the text content of the text area to obtain a text content set; inputting the text content set into a text large model, extracting structured information through preset prompt words, and obtaining first structured information; inputting the image into a visual language model, extracting structured information through prompt words, and obtaining second structured information; comparing and fusing the first structured information and the second structured information to obtain hotel structured information. The present invention can understand the image content more comprehensively and accurately, significantly improve the accuracy of hotel structured information extraction, and reduce the error rate of information extraction.

The invention discloses an intelligent contract risk recognition method and system, and relates to the technical field of text recognition, and the method comprises the steps: extracting a semantic vector of a to-be-recognized file; obtaining a first risk identification result based on the semantic vector and the review list; performing semantic matching on the semantic vector and the review knowledge graph to obtain a potential risk; obtaining a first risk category based on the potential risk and the review knowledge spectrogram, obtaining a preset risk judgment rule based on the first risk category, and obtaining a second risk category based on the preset risk judgment rule; obtaining a second risk identification result based on the contract category and the second risk category; constructing a clause rule base, and obtaining a compliance result based on the semantic vector and the clause rule base; obtaining a complete result based on the semantic vector and the standardized contract template library; and obtaining a total risk identification result based on the above identification result, thereby solving the problems of low risk identification efficiency and low accuracy caused by the fact that an existing contract term risk identification method depends on the determination of the license experience of professionals.

The invention discloses an engineering drawing label identification method and system based on multi-modal information extraction, and the method comprises the steps: obtaining an engineering drawing image, carrying out the preprocessing of the image, and setting a data structure Schema for controlling an extraction target and a relation mode; detecting a drawing label area in the drawing through the trained drawing label detection model to obtain a bounding box coordinate of each drawing label, mapping the detected bounding box coordinates back to a coordinate system of the original image, and cutting out a corresponding drawing label image from the original image; performing text recognition on the cut-out signature image, and extracting text content and corresponding textbox coordinate information; and inputting the clipped signature image and the text recognition result into a trained multi-modal information extraction model, performing information extraction according to a set Schema, and outputting a structured extraction result. According to the method, the extraction target can be flexibly defined, unified extraction of the two-tuples and the three-tuples is supported, and the extraction accuracy is high.

A system and method for automatically extracting data from tables and graphs in scientific literature, particularly in life sciences and healthcare, is presented. The invention employs a hybrid approach combining computer vision, natural language processing (NLP), and a large language model (LLM)-based data extraction module. A neural network enhances accuracy by providing contextual information. The system performs table structure detection, optical character recognition (OCR), Vision Transformers (ViTs) for text recognition, and graph-to- table conversion. An LLM refines the extracted data using advanced prompt engineering. A user interface enables data review, validation, and iterative refinement. The system employs a novel two-stage approach: de-rendering tables and graphs into a machine-readable format, followed by interpretation and user-defined mapping. A knowledge graph enhances extraction by resolving ambiguities and inferring relationships. Designed for scalability and continuous improvement, the invention significantly enhances data extraction efficiency and accuracy, accelerating research and knowledge discovery.

The invention relates to an app automatic execution and test system based on multi-agent collaborative perception decision, and the system comprises three agents and an upper computer which is used for collecting information and carrying out interactive execution: an execution decision agent receives the output of a planning strategy recommendation agent and a perception agent, serves as input information, generates an instruction to control the upper computer, and carries out the execution of the decision. And interacting with the to-be-tested system to complete the execution task of the to-be-tested system. The three agents are an execution decision agent, a planning strategy recommendation agent and a perception agent, and the three agents jointly form a decision system of the app automatic execution system. After a task target is given, the system can interact with a system to be tested, text recognition, icon recognition, keyboard recognition, layout recognition and anomaly recognition are carried out on a UI page where the system is located through a sensing agent, and meanwhile, system information is stored in a knowledge base after a planning strategy recommendation agent carries out offline exploration; and the execution decision-making agent autonomously reasones out an execution action according to information input of the perception agent and the planning strategy recommendation agent so as to complete execution of a task target. According to the method, multiple agents with different capabilities are combined to work cooperatively while the reasoning capability of the large model is utilized, so that the accuracy and the stability of the model are greatly improved.

The invention relates to the field of computer vision, and particularly provides a table recognition reconstruction method and system, a terminal and a medium, and the method comprises the steps: firstly decomposing a large-size table image into a plurality of overlapped sub-images, and carrying out the table structure detection and OCR character recognition of each sub-image through parallel recognition; then, sub-graph recognition results are integrated through a coordinate mapping and confidence coefficient weighted fusion algorithm, and boundary errors are eliminated; then, automatically distinguishing common cells based on an area clustering algorithm, merging the cells and a header region, and reconstructing a complete table logic structure; further understanding header semantics through a natural language model and repairing identification errors; and finally, realizing intelligent splicing and standardized output of the cross-page table. According to the method, the memory limitation of the traditional OCR technology is broken through, an oversized table can be processed, the recognition accuracy of a complex structure is improved, and the digitization efficiency of professional documents such as financial statements and engineering drawings is improved.

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 a dynamic data processing and recognition method and device for a mixed font text, and belongs to the technical field of optical character recognition, and the method comprises the following steps: collecting a mixed font text image to be processed, and inputting the mixed font text image into an enhanced DBNet detection network to position a text region; integrating a lightweight MobileNetV3 font classification sub-network, sharing bottom layer features with a detection trunk, training a multi-task network through a joint loss function, and outputting a text region with a font type label; then selecting identification branches according to the labels, and identifying the printed form by using the traditional CRNN; according to the handwritten form, traditional convolution is replaced by linear variable convolution, kernel dynamic scaling is guided by a thermodynamic diagram, and multi-head attention mechanism recognition is embedded in a BiLSTM layer; and finally, results are fused, and detection and identification results with labels are output. According to the method, the mixed font text data processing and recognition precision is improved, the adaptability and robustness of handwritten text recognition are enhanced, and various text recognition requirements are met.

The invention discloses a scene text recognition method based on optimized multi-modal vision and language processing, which comprises the following steps of: firstly, normalizing image data; and then inputting the preprocessed data into the optimized visual model. The visual model extracts multi-scale space and semantic features through a convolution-Transform hybrid neural network, and enhances the feature expression ability by using a multi-scale attention mechanism; the language model corrects character probability vectors output by the visual model, and learnable position codes are introduced to optimize representation of the features. Through designing a bidirectional multi-modal interaction module, visual and language features are fused, and a self-adaptive fusion mechanism is used to generate high-quality multi-modal joint feature representation. In the application stage, the optimized model is deployed through an efficient reasoning framework, and the speed and accuracy of scene text recognition are remarkably improved.

The invention provides a text recognition method based on a language model, and the method comprises the steps: obtaining a to-be-detected text data flow in a target application program, including user input, interface display and network transmission text; preprocessing the text data stream to generate a multi-dimensional semantic vector sequence; performing feature modulation on the semantic vector sequence through a dynamic weight distribution module to generate a modulation feature matrix; inputting a logic judgment engine to execute multi-stage condition combination analysis, and generating risk probability distribution; demodulating the risk probability distribution to generate an interpretable risk feature vector; executing cross validation based on the risk feature vector and a preset threshold set, and generating a final risk judgment result; and when a high risk is determined, triggering a multi-layer protection mechanism, including content replacement, session interruption and safety alarm. The accuracy and interpretability of the system can be improved, and the safety and user experience of the target application program are guaranteed.

The invention relates to the technical field of intelligent file processing, and discloses an element extraction method and device of a test report, computer equipment and a storage medium, the method comprises the following steps: obtaining scanning document documents of a plurality of paper test reports, and screening out a multi-modal format document from the scanning document documents; identifying and extracting text features, picture features and table features in the multi-modal format document based on an optical character identification technology model; performing paragraph combination on the extracted text features; and extracting structured data from the text features, the picture features and the table features after paragraph combination by adopting a multi-modal element extraction model to obtain a complete electronic document corresponding to the multi-modal format document. Based on an optical character recognition technology, character features, image features and table features in various paper test reports are accurately recognized and extracted by using a number, image and character recognition and extraction technology, and the recognition and extraction accuracy and extraction efficiency are improved.

The invention relates to a Lao image text recognition method and device fused with a language error correction model, and belongs to the field of natural language processing. Lao character structures are more complex and have numerous homomorphic words, and the method is provided in order to solve the problem that similar characters in Lao images are poor in recognition. The Lao text image recognition method comprises the steps of Lao text image data set construction, Lao image text recognition model construction fused with a language error correction model, Lao image text recognition model training fused with the language error correction model and Lao text image recognition. The Lao image text recognition device fused with the language error correction model is manufactured according to functional modularization of the four parts, and the problem that Lao characters are difficult to recognize due to similar font structures is effectively solved.

The invention provides an automatic legal instrument generation method and system based on a large model. The method comprises the following steps: establishing a legal provision index bound with a document type by synchronously collecting a paper document scanning document and legal provision update data; performing adaptive binarization processing on the scanned copy to enhance text recognition, and extracting pressure-sensitive signature ink permeation characteristics to generate an auxiliary mask; constructing a two-channel parser by utilizing a pre-trained large model, wherein a main channel parses a text to generate a semantic vector, and an auxiliary channel retrieves an association law article vector; dynamically screening vector interaction information based on an auxiliary mask, verifying the consistency of text semantics and signature positions in combination with ink features, and outputting structured elements with confidence; and finally, generating a first draft of the legal document, and outputting a compliant document after automatic checking and typesetting, clause quotation and item signing. According to the method, automatic generation of high-precision and highly-compliant legal instruments is realized through multi-modal signature verification and dynamic law article indexing.

The invention provides a character recognition method and device, computer equipment and a storage medium. According to the scheme, the slice scanning image is firstly acquired, then the label area is identified according to the slice scanning image, and the character information is close to the label, so that the determined target area is larger than the area of the label so as to cover the character information. And then the slice scanning image is intercepted according to the target area, and a target image only containing key information is obtained. And character recognition is carried out on the target image to obtain a character recognition result. And finally, the character recognition results are screened by using a rule base, and a target recognition result is screened out. According to the scheme, through tag region identification and target region interception, the character identification range is narrowed, the identification efficiency is improved, and the waste of computing resources caused by processing a large amount of irrelevant information is reduced. The rule base can better adapt to complex slice images and constantly changing requirements, information needed by a user is accurately screened out, and the character recognition efficiency is greatly improved.

The invention discloses a multi-modal large model-based seal detection and identification method and system, and the method comprises the steps: carrying out the size standardization processing of obtained document image data through an image preprocessing module, and generating a standardized image which accords with the input specification of a multi-modal large model; the standardized image and the structured recognition instruction are combined and then input into a multi-modal large model subjected to fine tuning training for end-to-end reasoning, and a recognition result character string conforming to the JSON format specification is output; and analyzing the recognition result character string to extract the bounding box coordinate, the type label, the text content and the text recognition confidence coefficient of each seal object, performing hierarchical decision processing based on the text recognition confidence coefficient, and finally outputting a structured recognition result. According to the invention, the accuracy and the automation level of seal identification are obviously improved, the normalization and the reliability of an output result are ensured, and the problems of low seal identification precision and non-standardized output in a complex scene are effectively solved.

The invention discloses a method for identifying an engineering drawing detail table and generating a BOM table, and belongs to the crossing field of automation technology and image processing, and the method comprises the steps: obtaining a scanning or electronic image of an engineering drawing; through preset datum line positioning, recursively detecting a nested rectangular region conforming to an area difference threshold value, and determining a title bar, a detail list region coordinate and a table image; identifying lines and cross points, analyzing the line and column boundaries of the table, and constructing a topological structure; performing character recognition by adopting multi-engine OCR integration; the characters and the cells are associated, a header is recognized through semantics, and analysis data subjected to integrity verification are generated; and automatically generating a structured BOM table in a preset standard format based on the data, and outputting an editable file. According to the method, full-process automation is achieved, manual intervention is not needed, and manual input cost and personal errors are greatly reduced.

The invention provides a personalized federal learning method for a pipeline instrument flow chart. When a PID drawing is used for knowledge graph model training, the problems of insufficient local training data and inconsistent joint training standards are often encountered, and the generalization ability and the individuation ability of the model are influenced. The method comprises the following steps: establishing a mapping relation between a local PID legend standard and a global PID legend standard by introducing a federated learning framework; constructing a triple by using the extracted knowledge corresponding PID legend standard by using a target detection and character recognition technology, and forming a local knowledge graph and a global knowledge graph; a federal learning method is used for training a knowledge completion model and a knowledge embedding model, drawing data from different enterprises are fully utilized, and the generalization ability of the models is improved while the privacy of the drawing data is protected; personalized training is carried out based on a federal training result, so that the extracted knowledge is ensured to benefit from other node data, and meanwhile, the personalized requirements of each enterprise are met.

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.

A structural building design system for processing, interpreting and analysing holistically a multipage set of two-dimensional (2D) real-world building construction plans for a building and yielding near real time accurate material type, quantity, and specification outputs as required for construction of a compliant building from said plans, through computationally generating a mathematical feature vector space dataset, the system comprising: one or more processors configured to: receive a two-dimensional real-world architectural plan for construction of a structural building, wherein the two-dimensional real-world architectural plan includes objects comprising: architectural symbols, lines, shading, or text; perform pre-processing, of characteristics on or associated with the objects on the two dimensional real-world architectural plan on a pixel by pixel basis for measurement or adjacent multi-pixel basis for object detection, wherein the pre-processing comprises two or more of: object detection and recognition, semantic segmentation, or text recognition to identify a plurality of objects, on the two dimensional real-world architectural plan to identify the characteristic features thereof; computationally transforming at least one characteristic feature of known (learned) and unknown (unlearned) detected objects into a mathematical representation thereof to form part of a future vector space dataset; and wherein said transformations for learned and unlearned detected objects have both high level and low level classifications of identified characteristic features; performing a comparison of the future feature vector space dataset for the identified detected objects to confirm the detected objects meet a predetermined confidence threshold for the classification of each detected object; performing a correlation analysis of the characteristic features for detected objects meeting the pre-determined confidence threshold level, wherein the comparison and correlation analyses above include one or more of determining shape, position, adjacent objects and using said pre-processing and results of correlation analysis of the feature vector space dataset via the algorithms to provide output information regarding the 2D plan including producing autonomous, and highly accurate creation of at least one or more of the following outputs: near real time accurate building takeoffs; complete construction estimates; complete construction detailing; detailed bill of materials for the construction of the building; or a document summarizing differences or similarities between building plans.

The invention discloses a text recognition method and system for financial index analysis based on image recognition processing, and relates to the technical field of financial analysis. An image is collected from a financial file, a traceability identifier is generated, format analysis and dual-channel recognition are conducted on the image, and a field fragment set is obtained; performing financial index positioning and aperture alignment according to the index dictionary and the aperture rule set, executing cross-voucher consistency check and performing missing field repair, generating a financial index evidence chain and outputting an auditing report, performing exception handling and updating the template library and the index dictionary, and obtaining an audit result; according to the method, standardization and integrity of financial data are realized through acquisition and preprocessing, format analysis and dual-channel identification, index caliber unification, cross-voucher consistency checking and deletion repair, an evidence chain and a difference view are generated, whole-course tracing and auditing are supported, stability is improved by combining exception handling and template library updating, and the method is suitable for popularization and application. And the identification accuracy and compliance are obviously improved.

The invention discloses a table data recovery method based on an OCR model, and the method comprises the steps: S1, recognizing table types through employing a lightweight model, and enabling the table types to comprise a wired table and a wireless table; s2, respectively acquiring a logic coordinate and a physical coordinate of a detection frame of the wireless table and a cell physical coordinate of the wired table; s3, recovering a logical structure of the table according to the detected table coordinates, and determining a row-column relationship of the table; s4, performing character recognition on the image by using a text recognition model, obtaining the position and content of a character box, performing accurate matching of characters and cells by calculating the coordinate overlap ratio of the OCR recognition character box and the cell box, and determining the character content in each cell; and S5, for the cells which are not matched with the characters, cutting the cell areas, carrying out secondary OCR identification on the image areas of the cells, and supplementing missing character contents. According to the method, the automatic identification and analysis capability of the complex table data is improved.

The present invention relates to engineering drawing recognition and OCR methods, text recognition models and electronic devices. The engineering drawing recognition method comprises the following steps: a drawing division step, detecting the information of the engineering drawing, and dividing the engineering drawing into a frame area and a view area; and a text information recognition step, respectively recognizing the text information of the frame area and the view area; wherein recognizing the text information of the view area comprises: training a text recognition model for recognizing the text information of the view area. Based on the complexity and particularity of the text information of the engineering drawing, the present invention divides the drawing into a frame area and a view area, and specifically trains a text recognition model for recognizing the text information of the view area through computer vision and natural language processing, and respectively recognizes and extracts the text information of each area, thereby realizing automatic extraction of drawing information, and at the same time improving the speed and accuracy of recognizing and extracting the engineering drawing information, thereby improving the speed of quoting to customers based on the drawings.

The invention provides an image text recognition method and system based on a mask diffusion model, a storage medium and equipment, and belongs to the technical field of image or video recognition or understanding. According to the method, multi-scale visual features of an image are extracted through a visual encoder, a mask diffusion decoder is combined, a diversified mask strategy and random character replacement disturbance are adopted in a training stage, denoising loss and auto-reflection loss are calculated respectively, and a model is optimized in a combined mode; in the reasoning stage, starting from a full mask state, a complete text sequence is recovered through multi-round iterative denoising. According to the method, the one-way modeling limitation of a traditional autoregression model is broken through, all-around context-dependent modeling is achieved, an autoreversion error correction mechanism and a block low-confidence mask strategy are introduced, and the recognition accuracy and reasoning efficiency in complex scenes such as shielding and fuzzy scenes are remarkably improved. The method provided by the invention reaches a leading level on a plurality of public data sets, and has the advantages of high precision and high speed.

The invention relates to the technical field of human resource management, and provides an intelligent paper marking system and method for talent selection and recruitment. The method comprises the following steps: cutting test paper according to a preset test paper template by using an image processing technology to obtain a plurality of plates corresponding to the test paper; converting the image content of each section of the cut test paper into a text format which can be edited and processed by adopting a character recognition technology; setting a preliminary scoring standard according to question type information and knowledge point information corresponding to the test paper, and optimizing the preliminary scoring standard by using a large language model; performing semantic understanding and logic analysis on the converted test paper text answers from a plurality of scoring dimensions including semantic accuracy, logic integrity and knowledge point coverage by using two preset scoring large models to give corresponding scoring information, and marking advantage information and defect information in the answers; and obtaining a comprehensive score corresponding to the test paper according to score information printed by each score big model for each question of each test paper.

The present disclosure relates to a homework grading method and apparatus, the homework grading method comprising: acquiring a question image, and performing text recognition on the question image to generate a text recognition result and a target matrix, the target matrix being formed on the basis of the sequence of answer texts comprised in the question image; using the text recognition result and the question image as inputs of a pre-trained multi-modal model to obtain an output result, the output result comprising a text to be graded of a target question in the question image and the question type of the target question; determining a grading mode corresponding to the question type, and, on the basis of said text, obtaining in the grading mode a reference text; and comparing the reference text with the answer text corresponding to the target question in the target matrix, so as to generate a grading result of the target question.

The invention discloses a process automation method and device and electronic equipment. The method comprises the following steps: acquiring a user interface image; target detection and text recognition are conducted on the user interface image through a visual recognition model, visual information corresponding to the user interface image is obtained, and the visual information comprises interface element information and text information in the user interface image; receiving user request information, and processing the user request information and the visual information through a semantic understanding model to generate an operation instruction sequence; and converting the operation instruction sequence into an operation instruction, and automatically simulating a user operation behavior according to the operation instruction. According to the method and the device, the technical problems of relatively poor interface adaptability, relatively weak user intention understanding ability and insufficient robustness in a complex scene due to the fact that an RPA system in related technologies is generally based on fixed interface coordinates or control features are solved.

The invention provides a construction engineering bidding document automatic generation method and system, and belongs to the technical field of engineering construction. The method comprises the following steps of: preprocessing enterprise data, extracting information used in a bidding process from an unstructured enterprise data document by utilizing character recognition and natural language processing technologies, converting the information into structured data, and constructing a dynamic knowledge graph; generating a business bidding document based on a big language model according to the bidding document; constructing a technical scheme knowledge base, generating a response book and a technical scheme based on the technical scheme knowledge base and the bidding document, and integrating the response book and the technical scheme to generate a technical bidding document; and constructing an evaluation matrix, and evaluating the business bidding document and the technical bidding document. According to the method, the compiling efficiency and quality can be improved, and manual errors and time cost are reduced.

The invention relates to the field of character recognition systems, and discloses a Chinese ancient book character recognition method based on an image recognition technology, which comprises the following steps: reading an ancient book image according to an open source code computer vision library to obtain an original image; performing gray processing on the original image to obtain an image after gray equalization; according to a median filtering algorithm, carrying out de-noising processing on the image after gray scale equalization to obtain a pre-processed image; according to the method, multi-level feature extraction is carried out on the image through the real-time multi-scale detection model and the convolutional neural network, and character features of different scales and angles in the image can be captured; and therefore, the character recognition accuracy is improved, especially for the common font, typesetting, inclination or blurring conditions in ancient book images.

The invention discloses an engineering design document compliance inspection method, electronic equipment and a storage medium, and the method is based on an OCR and a large language model, and comprises the following steps: standardizing the character recognition and extraction of a document; standardizing character error check and manual correction of the text; performing structured processing on the corrected standard text; vectorization processing of core text content and construction of a standard query system; presetting a specification set range corresponding to each chapter of the design document; carrying out vectorization and similarity query on characters selected by a user in the text editor; corresponding standard content is provided for the user; the method can improve the efficiency and accuracy of specification reference in the document, facilitates the dynamic update management of specification contents, and facilitates the guarantee of the correctness of reference specifications.