1362 results about "Text recognition" patented technology

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 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 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 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 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 industrial drawing analysis method and system combined with a multi-modal large model and OCR, and relates to the technical field of drawing analysis, the method comprises the following steps: carrying out layout area segmentation, text recognition and geometric element extraction on an industrial drawing to obtain a structured information set; constructing a multi-relation structure chart set of the industrial drawings; performing structure embedding and feature bias enhancement to obtain a structure feature set; performing semantic understanding on the industrial drawing to obtain semantic features, and performing multi-channel coding to obtain a multi-modal feature set; obtaining a preliminary analysis result set of the industrial drawing; and performing rule verification, and outputting an industrial drawing analysis result. The technical problems of low industrial drawing analysis efficiency, inaccurate automatic scheme analysis and limited processing capacity in the prior art are solved, and the technical effects of realizing full-process automatic analysis of the industrial drawing by combining the multi-modal large model and the OCR, improving the precision and efficiency of industrial drawing analysis and standardizing output information are achieved.

The invention relates to the technical field of computer vision, image recognition and intelligent medicine management, in particular to a medicine bottle label content recognition method based on multiple cameras and YOLOv8. The method at least comprises the following steps: S1, deploying a medicine bottle label generation system, and generating a label; s2, multi-camera image acquisition and preprocessing; s3, carrying out chessboard calibration and space positioning; s4, medicine bottle label detection and label character recognition and structured analysis; and S5, system integration and application. According to the invention, through combination of multi-camera and multi-angle acquisition and checkerboard calibration positioning and combination with YOLOv8 label detection and OCR identification, high precision, high efficiency, end-to-end automation and system integration of medicine bottle label identification are realized, the defects of precision, efficiency, environmental adaptability and management integration in the prior art are overcome, and the system is suitable for popularization and application. The method has obvious technical advantages and practical value.

The invention provides an identification detection method and system for a radio frequency connector, and the method comprises the steps: obtaining the initial appearance image data of the surface of the radio frequency connector, and carrying out the processing of the image data, and obtaining a first appearance image; extracting an appearance feature set and an identification area image from the first appearance image; performing character segmentation and feature extraction on the identification area image, obtaining a preliminary manufacturing information text in combination with a pre-established text recognition model, verifying the preliminary manufacturing information text and associating the preliminary manufacturing information text with the appearance feature set to generate an associated data set; comparing the appearance feature set with a preset standard appearance database to generate defect marking data, fusing the associated data set with the defect marking data to generate comprehensive analysis data, and generating a visual chart according to the comprehensive analysis data; and further analyzing performance index change and defect severity according to the visual chart, generating risk assessment data, and generating a final detection analysis file according to the risk assessment data.

The invention discloses a multi-mode interactive virtual teaching method and system. The method comprises the following steps: acquiring multi-source data; extracting voice data acoustic features, and inputting the voice data acoustic features into a learning model to obtain a text triple; key terms are extracted from the text data, a traceable operation chain is generated, semantic analysis is carried out, and an operation scheme is output in combination with a knowledge graph; performing abnormal state recognition on the image data through a target detection model, positioning abnormal equipment in combination with a character recognition model, performing action mapping through gesture recognition and a spatial constraint rule, and outputting a corresponding instruction; fusing the three types of outputs to generate a scheduling event chain; and performing semantic analysis, evaluation and optimization on the scheduling event chain, interacting with personnel, updating operation suggestions and providing an operation analysis result. According to the method, manual rechecking requirements are reduced through multi-modal data collaboration, a new man-machine interaction database and a new man-machine interaction standard in the power industry can be formed through multi-modal interaction rules and event chain construction, and data intelligent driving is achieved while the training efficiency is improved.

The invention discloses a character recognition method and system based on a large model and an OCR technology, and relates to the technical field of character recognition, and the method comprises the steps: extracting picture information, carrying out the unified preprocessing, generating a text detection box of a character region through a DBNet lightweight text detection model, and obtaining a text position; quickly identifying characters in the textbox by using a lightweight OCR model to obtain text content, and generating an identification information group; carrying out average value calculation on the confidence coefficient of the lightweight OCR model result, and analyzing the overall confidence coefficient; and for the result with low confidence coefficient, inputting the corresponding identification information group into the multi-modal large model, and carrying out secondary identification. According to the method, the text position set and the text character string sequence are combined into the identification information group, so that the effect of structured storage of detection and identification results is achieved, subsequent information retrieval and multi-modal fusion analysis are facilitated, and the effect of optimizing the subsequent processing efficiency and precision is achieved through the combination of the confidence coefficient screening step.

The invention discloses a scene text recognition method with residual attention and scale perception. The method comprises the following steps: acquiring an original input image and preprocessing the original input image; extracting multi-scale features of the preprocessed image through a residual attention and scale perception encoder, and enhancing text region response through a residual attention mechanism in the feature extraction process; modeling the coding feature sequence into character semantic representation through a multi-scale decoding attention module; the decoded output is mapped to a character category to generate a predicted text sequence. According to the method, a residual attention mechanism and a scale perception structure are introduced, so that the extraction capability of a model on key features and the modeling capability of a multi-scale text are effectively enhanced, and the robustness and generalization performance of recognition are improved.

The invention relates to the field of drawing recognition, in particular to a PDF drawing data extraction method and system based on intelligent recognition. Comprising the following steps: reading an internal structure of a PDF engineering drawing to obtain a native text stream, a vector path and a grating image; identifying the native text flow through a shunt preprocessing framework to form structured text data; rendering the vector path and the grating image to obtain a background image; analyzing the structured text data by utilizing the intelligent recognition model through the character recognition and extraction sub-model, obtaining drawing metadata and recording the position, and obtaining a character recognition result; analyzing the background image through a graphic element recognition and classification sub-model, recognizing and classifying component elements, and obtaining a graphic recognition result; and performing fusion according to the visual space corresponding relation to form a drawing analysis result. According to the method, the adaptive capacity of engineering drawings with various sources and different qualities is improved through the shunting preprocessing framework and the intelligent identification model.

The invention belongs to the technical field of artificial intelligence, and relates to a sensitive information-based file tracing method, which comprises the following steps of: performing text recognition on a to-be-traced file based on a text content recognition technology to obtain a content recognition result; performing sensitive information matching on the content identification result through a preset sensitive information identification strategy; determining that the file to be traced contains sensitive information, generating sensitive file identification information and recording user information; in response to an operation behavior of a user on the to-be-traced file, obtaining operation information, and calling an integration tool to inject the user information and the operation information into the to-be-traced file to generate file integration information; and generating a traceability chain based on the sensitive file identification information and the file integration information. The invention further provides a file traceability device and equipment based on the sensitive information and a storage medium. The method can be applied to business management program systems of financial science and technology, medical treatment and the like, the source, the circulation path and the final destination of the file can be clearly presented, and full-life-cycle management of the file is achieved.

The invention provides an orthopedic implant coded character recognition method based on machine learning, and the method comprises the steps: extracting a deformation parameter of a character region according to a surface curvature distribution diagram, determining a deformation proportionality coefficient and an angle deviation matrix, and fusing a spiral curvature radius to evaluate a path torsion coefficient; performing reverse adjustment on the angle deviation matrix by adopting an angle correction algorithm to obtain a corrected character angle parameter, and verifying the stability of the curvature peak position through a path continuity index; if the character integrity is higher than a preset threshold value, directly extracting a coding sequence to obtain preliminary coding information, and adjusting an offset correction factor according to curvature distribution density to optimize a spiral pitch parameter; and performing geometric reduction on the coding sequence through the character arrangement direction to obtain a final recognition result. According to the method, the recognition accuracy of complex curved surface characters is remarkably improved, the robustness of coding sequence extraction is optimized, and an efficient solution is provided for reliable recognition of implant surface characters.

The invention discloses a PDF document conversion device and method, a storage medium and computer equipment. The device comprises a data processing module which converts a PDF document into a to-be-processed image and obtains original image data of an embedded image; the layout analysis module performs region division on the to-be-processed image, and determines a text region, a formula region and a table region of the PDF document; the first recognition module performs content recognition on the text region, the formula region and the table region to generate a text recognition result, a formula recognition result and a table recognition result; the second recognition module generates an image recognition result based on the original image data; the semantic analysis module generates semantic representations of a text recognition result, a formula recognition result, a table recognition result and an image recognition result through a large language model; and the PowerPoint generation module maps the obtained recognition result and semantic representation to a PowerPoint template to generate a final PowerPoint. The accuracy and content richness of generating the presentation file based on the PDF document can be improved.

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 present disclosure provides a character recognition model training method and apparatus, a character recognition method and apparatus, a device and a medium, relating to the technical field of artificial intelligence, and specifically to the technical fields of deep learning, image processing and computer vision, which can be applied to scenarios such as character detection and recognition technology. The specific implementing solution is: partitioning an untagged training sample into at least two sub-sample images; dividing the at least two sub-sample images into a first training set and a second training set; where the first training set includes a first sub-sample image with a visible attribute, and the second training set includes a second sub-sample image with an invisible attribute; performing self-supervised training on a to-be-trained encoder by taking the second training set as a tag of the first training set, to obtain a target encoder.

The invention provides an AI text recognition method and device based on ensemble learning and advanced semantic statistical feature analysis, and the method comprises the steps: 1, respectively sending a to-be-recognized text into a Bert detector and a high-order natural language statistical feature detector for recognition, the high-order natural language statistical feature detector comprises a word logarithm probability detector, a word ranking logarithm detector, an Entropy detector and a confusion degree detector; and 2, performing election on detection results output by the Bert detector and the high-order natural language statistical feature detector by using an election module to obtain an AI text recognition result. According to the method, an integrated learning strategy is adopted, and a pre-training language model subjected to fine tuning is combined with high-order natural language statistical characteristics, so that when the model detects a large language model to generate a text, the strong expression ability of the pre-training language model can be fully utilized, and a deep rule of the text can be captured through the high-order statistical characteristics; and the detection accuracy is improved.

The invention discloses a video character recognition and erasing method and system, a storage medium and an electronic device. The method comprises the following steps: acquiring a video; performing frame extraction processing to obtain a video frame picture; the method comprises the following steps: acquiring all text contents and coordinate data in a picture through OCR (Optical Character Recognition) identification, analyzing and identifying flower characters and subtitles on a video frame picture through a multi-modal large model, screening out a region of the flower characters and the subtitles needing to be erased through coordinate matching, and determining the region as an erased region; and intelligently erasing and repairing the flower and subtitle areas by adopting a video repairing technology, recovering the original state of the video, and generating an erased new video file. According to the method and the device, the full process of automation is realized, subtitles and flower characters in the video do not need to be manually marked, identified and erased, printed characters of commodities / packages are reserved, mistaken erasure is avoided, and the processing efficiency of video reediting is greatly improved.

The invention relates to a text recognition method and device, computer equipment, a storage medium and a computer program product. The method comprises the following steps: acquiring a to-be-recognized image; inputting the to-be-recognized image into a visual network of the text recognition model, and extracting visual features of the to-be-recognized image; inputting the visual features into a language network of a text recognition model, and extracting semantic features based on the visual features through the language network; inputting the visual features and the semantic features into a fusion network of a text recognition model, and performing fusion processing on the visual features and the semantic features through a plurality of fusion units in the fusion network to obtain a fusion result; and generating a text recognition result for the to-be-recognized image based on the fusion result. By adopting the method, the text contained in the image can be accurately identified.

The invention discloses a text recognition model training method and device, electronic equipment and a storage medium. The method comprises the steps of performing text prediction on a first training sample set of a current training round based on a first text recognition model of the current training round to determine a first prediction result and a first confidence coefficient of the first prediction result; selecting a first reference sample from each first training sample based on each first confidence coefficient; adjusting parameters of the first text recognition model based on a first loss value determined by the first prediction result to obtain a second text recognition model of the next training round; and determining a second reference sample based on the first recognition model and the first reference sample, adding the first reference sample and the second reference sample into the first training sample set to obtain a second training sample set of the next training round, and training a second text recognition model based on the second training sample set. According to the method, the problems of low text recognition accuracy and insufficient generalization ability of a text recognition model in a specific scene are solved.

The present disclosure provides a network model training method, a data processing method, and an apparatus. The network model training method comprises: acquiring target sample data, wherein the target sample data comprises text sample data and image sample data; inputting the target sample data into a network model to be trained to obtain a sample recognition result; and adjusting a parameter of a text encoder on the basis of a text recognition result and first supervision data corresponding to the text recognition result, adjusting a parameter of an image encoder on the basis of an image recognition result and second supervision data corresponding to the image recognition result, and a hybrid image-text recognition result and third supervision data corresponding to the hybrid image-text recognition result, and adjusting a parameter of a hybrid encoder on the basis of the hybrid image-text recognition result and the third supervision data corresponding to the hybrid image-text recognition result to obtain the trained network model formed by the text encoder, the image encoder, and the hybrid encoder.

The invention provides a teacher evaluation system based on a large language model, and relates to the technical field of teacher evaluation, and the system comprises a user interaction and teaching resource uploading module which is used for guiding a user to upload teaching resources; the teaching resource preprocessing and feature engineering module is used for performing text recognition and deep semantic proofreading and information enhancement processing based on large language model driving on teaching resources, and further integrating and managing the teaching resources to obtain teaching event core data; the multi-level intelligent expressive evaluation module based on the large language model is used for selecting a large language model combination, guiding the large language model combination to complete teacher evaluation through enhanced thinking chain CoT reasoning, a knowledge graph and cue words in a corresponding situation, performing analysis by using an intelligent agent, generating an explanatory or reflective text, and outputting the explanatory or reflective text. Recording an inference basis or a reference source; and the dynamic construction and visual presentation module is used for forming a teacher performance evaluation report and presenting the teacher performance evaluation report to the user. Therefore, the invention creates a novel intelligent evaluation system.

Disclosed are techniques for identifying and differentiating AI-generated text within a document. The system may capture added text, compare it to known AI-generated text using word-for-word comparison and vector analysis, and may highlight identified AI-generated text. It may also include a verification process to confirm whether the AI-generated text has been adequately reviewed. A user interface may allow users to modify properties of the text, attach review notes, and record changes to text. The system may be applicable in various scenarios, such as legal briefings, academic assignments, and artificial intelligence model training.