3086 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 belongs to the field of artificial intelligence and financial science and technology, and relates to a document segmentation method, which comprises the following steps: carrying out style feature recognition and title text recognition on a to-be-segmented document to obtain a title style result and a title text result, and then carrying out recognition verification on the title style result and the title text result through a multi-modal title recognition large model to obtain a segmented document. And obtaining a final title recognition result, constructing tree hierarchical representation based on the title recognition result to obtain a document chapter structure, and segmenting the to-be-segmented document according to the document chapter structure. The invention further provides a document segmentation device, computer equipment and a storage medium. In addition, the invention also relates to a block chain technology, and the to-be-segmented document can be stored in a block chain. According to the method and the device, the document is segmented in a chapter form, the semantic integrity of segmentation is ensured, and key information is effectively extracted and structured.

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.

The invention discloses a medical form data identification method and system based on OCR and MLLM, and the method comprises the steps: receiving original image data, and generating an optimized image sequence through adaptive contrast enhancement, regional importance weighting and multi-resolution optimization; feature extraction is carried out based on the optimized image sequence, a text-space attention map is constructed, and text recognition is realized; performing visual coding and text coding on the optimized image sequence and the text recognition result, and obtaining uniform feature representation through feature alignment; constructing an information level diagram based on unified feature representation, performing relation reasoning, and combining professional knowledge verification to obtain structured feature information; and performing data verification on the structured feature information to obtain normalized data. According to the invention, efficient identification and accurate structuring of the medical form are realized, and the digitization efficiency of the medical data is improved.

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 voice dialogue method and device, equipment, a storage medium and a product, and relates to the technical field of cloud computing, and the voice dialogue method comprises the steps: obtaining voice information input by a user and historical dialogue information of the user, and carrying out the text recognition of the voice information, and obtaining initial text information; based on the historical dialogue information and the initial text information, performing context matching through a preset text matching model to obtain matched target text information; generating prompt words corresponding to the target text information; and inputting the cue word into a preset large language model, and receiving and outputting answer voice information output by the large language model. According to the method, context matching is carried out on the historical dialogue information and the initial text information to obtain the target text information of the complete context obtained by contacting multiple sessions, so that a large language model can more accurately distinguish the actual intention of a user, and the voice dialogue effect is improved.

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 discloses a voice recognition and model training method, device and equipment and a computer program product, and relates to the technical field of voice recognition, a voice recognition model and a text reconstruction model are jointly trained, the voice recognition model and the text reconstruction model share the same decoder, the text reconstruction model further comprises a text encoder constructed on the basis of a large language model, and the voice recognition and model training efficiency is improved. According to the method, the text semantic representation can be extracted from the recognition text label, the feature alignment loss value between the audio semantic representation extracted from the audio sample by the audio encoder and the text semantic representation is calculated, the text recognition loss value between the text output by the decoder and the recognition text label is calculated, the large language model can extract rich semantic information, and the recognition accuracy is improved. Rich semantic information of the large language model can be migrated to the audio encoder through the feature alignment loss value, and the recognition effect of the speech recognition model in the specific field is improved. In the reasoning stage, the acoustic features of the voice signals to be recognized are input into the voice recognition model, and a voice recognition result output by the model is obtained.

The invention relates to the technical field of image-text recognition, in particular to a big data image-text recognition system and method based on artificial intelligence, and the system comprises an element identification module, a text keyword extraction module, a mapping network construction module, an image-text perception adjustment module, a decision diagram construction module and a format adaptability adjustment module. In the invention, through deep pixel analysis and visual feature matching, accurate analysis of the relationship between the object in the image and the visual label is realized, the accuracy of image-text data identification is improved, the specific association between the image content and the related text data is accurately understood, and the constructed image network is subjected to semantic association calculation, so that the accuracy of image-text data identification is improved. According to the method, the semantic model can dynamically adapt to new data input, the decision path of the image-text content is optimized, the image-text recognition efficiency is effectively improved, the classification decision is more accurate, the image-text recognition quality and speed in the big data environment are optimized, and the processing performance and the application flexibility are remarkably improved.

A deep neural network-based system for detection and recognition of construction elements in construction engineering drawings. A first neural network performs object detection and recognition to detect and recognize ducts and fittings. A second neural network performs text detection to detect text. A third neural network performs text recognition. Pixel tracing of the construction engineering drawing is performed using the bounding box for detected text as a reference point. The recognized text is filtered to output system types and system sizes from the recognized text. Each system type and size instance is correlated to a detected duct, pipe or fitting instance to determine object coordinates, system type, item type, size and length. A list of construction elements is generated for all detected duct, pipe and fitting instances. An interactive visualization of the construction engineering drawing based on the list of construction elements and / or a costing may be provided.

Various approaches relate to user defined content filtering in media playing devices of undesirable content represented in stored and real-time content from content providers. For example, video, image, and / or audio data can be analyzed to identify and classify content included in the data using various classification models and object and text recognition approaches. Thereafter, the identification and classification can be used to control presentation and / or access to the content and / or portions of the content. For example, based on the classification, portions of the content can be modified (e.g., replaced, removed, degraded, etc.) using one or more techniques (e.g., media replacement, media removal, media degradation, etc.) and then presented.

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 crowdsourcing acquisition map data desensitization method and system based on deep learning. According to the method, a'high-precision map desensitization rule and target sensitivity level table 'is constructed for grading and classifying a to-be-desensitized target, and a desensitization method is specified and comprises a shape type, a privacy type and a character type. For shape type and privacy type sensitive targets in a high-precision map, the positions of the sensitive targets are detected by using a sensitive target detection algorithm based on Faster RCNN, and different desensitization methods are used according to the sensitive levels of the sensitive targets. For character sensitive targets in a high-precision map, all character areas in the map are detected by using a natural scene text detection model I3CL, the text areas are recognized by using a text recognition model SVTR to obtain text contents of the character areas, sensitive characters in the character areas are matched through a sensitive word matching module, and the sensitive characters in the text areas are matched through a sensitive word matching module; and a sensitive area removing model based on LaMa is used for the sensitive character area, and sensitive characters are removed from the graph.

The invention discloses a courseware content analysis method and device, equipment and a storage medium. The method comprises the following steps: acquiring a courseware picture; detecting each frame of courseware picture, and detecting a title and a text; inputting the detected title and text of each frame into a pre-trained character recognition model, and recognizing a title keyword set and a text keyword set of each frame of courseware picture; summarizing the title keyword set and the text keyword set of all frames of courseware pictures to obtain a courseware keyword set; obtaining a knowledge point set of a course corresponding to the courseware; obtaining a courseware integrity analysis result according to the matching-union ratio; and obtaining a courseware content analysis result according to the courseware integrity analysis result. According to the method, the teaching courseware content is analyzed and evaluated according to an objective specification standard, so that a scientific and objective evaluation conclusion is obtained.

Embodiments of the present description provide an image text recognition method and a feature decoding model training method. The image text recognition method comprises: acquiring an image to be recognized, wherein said image comprises text information; performing feature extraction on said image to obtain a target feature map corresponding to said image; on the basis of the target feature map, analyzing character position information in the text information to obtain a target character feature corresponding to said image; and on the basis of the target character feature, determining a text recognition result in said image. By analyzing a positional relationship between text characters in a target feature map, the positions of characters in text information, comprising later characters in a long text, can be accurately positioned, thereby improving the robustness of image text recognition in the length of a text.

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 provides a system and method for power data classification and arrangement, and relates to the technical field of power data management, and the method comprises the steps that a semantic fusion module integrates multi-source power archive data, carries out semantic matching and fusion, and generates a to-be-classified data set; the entity recognition module extracts classified data from the database and recognizes key entities by using a text recognition model; the incidence relation obtaining module identifies and corrects the incidence relation between the entities; the knowledge graph construction module constructs an electric power archive knowledge graph based on the entity and the association relationship; the classification model acquisition module trains an intelligent classification model, and the classification and arrangement module performs classification and arrangement on the target data set. According to the method and the device, the problem of low efficiency of power archive classification due to inaccurate entity identification of multi-source data and poor accuracy of the association relationship in the prior art can be solved, the accuracy and efficiency of power archive data classification are improved, and the automation level of power archive classification is improved.

The invention provides an automatic software testing method and system based on a visual attention mechanism, and relates to the technical field of automatic testing, and the method comprises the steps: obtaining an initial interface of to-be-tested software, carrying out the preprocessing, inputting an enhanced interface image into an interface detection model, and recognizing control information through the analysis of the enhanced interface image; inputting the control information into a pre-trained attention mechanism model, generating semantic association and context information between controls based on the control information, determining an execution sequence and execution parameters of test actions, sequentially simulating manual test actions, and performing test operation on the controls according to the execution parameters; interface feedback information of the to-be-tested software is obtained, text content in the interface feedback information is extracted based on a character recognition algorithm, semantics corresponding to the text content are determined in combination with a pre-constructed semantic understanding model and compared with a preset expected result, and a test conclusion corresponding to the test operation is generated.