3294 results about "Character 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 invention discloses a knowledge graph construction method and system based on traditional Chinese medicine classics, and relates to the technical field of knowledge graph construction, and the method comprises the steps: collecting multi-source heterogeneous data of traditional Chinese medicine classics, carrying out the standardization processing, and generating a cross-time aligned standardized corpus through optical character recognition and multi-modal feature extraction; based on a language model and a causal reasoning engine, extracting an entity relationship and a dialectical rule from the standardized corpus, and generating a dynamic dialectical reasoning model supporting nonlinear reasoning; obtaining an output result of the dynamic dialectical reasoning model, and constructing a redundancy-removed and dynamically-calibrated traditional Chinese medicine knowledge base through knowledge fusion and conflict resolution of various traditional Chinese medicine classics; based on edge calculation and privacy protection technologies, real-time iteration and clinical curative effect feedback optimization of the knowledge graph are realized. According to the method, the problems of trans-era evolution, regional expression difference, multi-modal feature splitting and the like in the traditional Chinese medicine classics digitalization process are solved.

Various embodiments of the present disclosure relate to operator assistance based on extracting natural language characters from one or more sensed objects. For instance, particular embodiments may generate a natural language utterance based on extracting natural language text in a nearby traffic sign. In an illustrative example, particular embodiments may detect, via object detection and within image data, one or more regions of the image data depicting the traffic sign. Particular embodiments can then extract one or more first natural language characters represented in the traffic sign based at least on performing optical character recognition within the one or more regions of the image data in response to detecting the one or more regions of the image data depicting the traffic sign.

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.

A computer implemented method, system and non-transitory computer-readable device for a remote deposit environment. Upon receiving a user request, based on interactions with the UI, the method implements an electronic deposit of a financial instrument by activating a camera on the client device to generate a live stream of image data of a field of view of at least one camera, wherein the live stream includes imagery of at least a portion of the financial instrument. The method continues by extracting in real-time, based on the formation of byte array objects from the live stream of image data, data fields from a ranked sequence of imagery to be processed by an optical character recognition (OCR) program resident on the client device. The OCR process extracts one or more data fields from of the financial instrument that are communicated to a remote deposit server to complete the electronic deposit.

A method including receiving a digital image including text arranged in a layout. The method also includes generating, by an optical character recognition model, a layout text vector that encodes at least one word in the text of the digital image and a position of the at least one word in the layout of the digital image. The method also includes generating, by a visual encoder model, a visual representation vector embedding a content of the digital image. The method also includes converting both the layout text vector and the visual representation vector into a projected text vector including a digital format suitable for input to a large language model. The method also includes combining, into a prompt, the projected text vector, a system message, and a task instruction. The method also includes generating an output including a key-value pair.

The invention provides an AI-based automatic identity card image cutting and OCR recognition method and system, and relates to the technical field of image processing, and the method comprises the steps: collecting a multi-angle depth image and a visible light image, reconstructing a three-dimensional model of a certificate through a three-dimensional point cloud registration network, and achieving the perspective correction of an image through the combination of attention posture estimation and projection matrix optimization; a double-flow memory neural network is adopted to carry out information area positioning, a feature extraction backbone network constructs a multi-scale feature pyramid, and a dynamic memory updating network carries out feature matching and dictionary updating based on a time sequence attention module and comparative learning; and performing character recognition on the segmented information sub-images through a multi-branch feature enhancement network, improving the recognition precision in combination with a self-attention enhancement unit and a context reasoning unit, and dynamically optimizing network parameters by using a reinforcement learning controller until a preset recognition credibility threshold value is reached.

A method can include receiving a document associated with a checklist item. A method can include identifying a template associated with the document. A method can include identifying an area of the document associated with a value of a key-value pair based at least in part on the template. A method can include extracting, using a first optical character recognition process, the value from the area. A method may cause display of the value. A method can include receiving a corrected value from the user. A method may include extracting, using a second optical character recognition process, full text of the document. A method may include determining a location of the corrected value in the document. A method may include updating the first optical character recognition process, wherein updating the first optical character recognition process causes the first optical character recognition to extract the value from the location.

The invention relates to the technical field of image recognition, in particular to a license plate recognition system and method based on an image technology and a medium. The method comprises the following steps: acquiring area sensing data and a camera image set, and performing deformation effect compensation to obtain an environment compensation image set; performing image diffusion reverse enhancement on the environment compensation image set to obtain a license plate area enhanced image set; performing character region high-dimensional topological mapping based on the license plate region enhanced image set to obtain a character segmentation matrix; extracting character morphological characteristics according to the character segmentation matrix, and performing character recognition on the character morphological characteristics to obtain a character recognition result; and carrying out cross-character semantic compensation on the character recognition result to obtain a semantic compensation license plate character vector, and carrying out multi-target cross verification on the semantic compensation license plate character vector to obtain a license plate recognition result. According to the invention, the accuracy and robustness of license plate recognition can be improved.

The invention relates to the technical field of text travel data analysis, in particular to a text travel content recommendation system for analyzing preferences of text travel scenic spots, which comprises a culture gene bank construction and user interest modeling unit and a dynamic situation awareness and recommendation optimization unit. The data acquisition and preprocessing module extracts culture elements from a multi-channel text through optical character recognition and natural language processing, the culture gene extraction and fusion module constructs a knowledge graph and fuses similar elements to form a gene cluster, and the user interest model training module generates interest portraits based on user text travel platform data. In a dynamic situation awareness and recommendation optimization unit, a situation data acquisition and analysis module acquires and analyzes user situation data, a recommendation strategy generation and adjustment module generates a recommendation strategy according to a user state and interest by using a deep Q network algorithm, recommendation is optimized in combination with situation factors, and the accuracy of travel content recommendation is effectively improved.

The automatic tax declaration and recheck system comprises a character recognition module, a process automation module, a dynamic rule engine module, a block chain evidence storage module and a distributed database. The character recognition module forms a tax field signal, a structured signal and a semantic tag signal. And the process automation module forms a tax declaration form according to the tax field signal. And the dynamic rule engine module receives the structured signal, performs multi-dimensional verification through a machine learning model, and forms a verification signal. The block chain evidence storage module comprises a distributed database, and the block chain evidence storage module receives the semantic tag signal and forms a unique hash value. And the distributed database performs feature fusion on the unique hash value, the tax declaration form and the verification signal to form a declaration signal and stores the declaration signal in the distributed database. According to the automatic tax declaration and rechecking system, the problem that structural analysis of multi-format tax documents is difficult can be solved.

In an embodiment, a method for converting a set of legal documents of varied type related to a legal task into structured data suitable for the legal task is disclosed. The method uses artificial intelligence and is performed by at least one processor including hardware. The method includes receiving digitized versions of a plurality of legal documents from a user. The method further includes extracting electronic data from the digitized legal documents using optical character recognition and analyzing the extracted electronic data from each document using a sequence of microprompts to a trained large language model. The analyzing includes using the LLM to identify a type of the document and data keys within the electronic data, and to generate a name for the document. The method further includes storing and sending to the user the analyzed electronic data processed with the LLM as structured data suitable for the legal task.

The invention relates to an engineering document index consistency proofreading method and system based on a multi-modal large model, and the method comprises the steps: Q1. OCR detection and recognition: carrying out the optical character recognition and format analysis of a source document, converting an uploaded PDF document into a processable text message in a Markdown format, and carrying out the format discrimination of a table, a formula and a plain text; and Q2, table and formula processing: adopting a hierarchical processing strategy, intelligently selecting an optimal processing mode according to the complexity of the table, and converting table information into a descriptive long text through a language large model and cue words. According to the method, accurate, reliable and efficient document index checking service can be provided for a user, the quality and efficiency of professional document processing are remarkably improved, the efficiency and quality of knowledge graph construction are remarkably improved, a knowledge verification system capable of being evolved continuously is established, and the method is suitable for popularization and application. And a reliable technical support is provided for knowledge management and professional decision-making in a complex field.

The invention provides a multi-modal document content cross-platform analysis system, and relates to the technical field of data processing, and the system comprises a document preprocessing module which is used for receiving multi-source heterogeneous document input data, and generating a preprocessed document through format conversion, page segmentation, noise reduction and optical character recognition; the feature extraction module is used for extracting three types of features, including spatial layout features, semantic features and logic structure features, based on the preprocessed document; according to the method, the multi-source heterogeneous document is cooperatively processed, accurate extraction, calibration and association of features in cross-platform analysis are realized, finally structured data are generated, and the accuracy, consistency and cross-platform applicability of multi-modal document analysis are improved.

The invention provides an intelligent retrieval matching method and system for multidimensional data of image-text features, and relates to the technical field of icon image retrieval. Comprising the following steps: extracting image features, text content and semantic features of an icon image by using a convolutional neural network, an image segmentation attention mechanism network, a converter optical character recognition model and a bidirectional semantic understanding model, constructing the extracted features into heterogeneous feature tensors, and performing singular value decomposition to obtain icon feature fingerprint vectors; and constructing a multi-level index based on locality sensitive hashing, realizing rapid retrieval, calculating visual, text and semantic similarities in combination with a deep metric learning model, weighting according to variances and discrimination coefficients of similarity features to obtain a comprehensive similarity score, and outputting a retrieval result with the highest similarity.

The invention discloses a character recognition and automatic auditing method and system, and the method comprises the steps: obtaining all to-be-audited document data including semi-structured and non-structured document data, guaranteeing the comprehensiveness of an auditing process, accurately recognizing the document content through an OCR technology, converting the document content into operable structured data, and carrying out the automatic auditing of the to-be-audited document data. Key information in a document is selected through two modes of rule extraction and model extraction, the efficiency and accuracy of information processing are improved, the information is compared with pre-established target structured text data, compliance check is automatically completed, the compliance of an auditing result is ensured, a confidence coefficient threshold value is updated in real time according to the complexity of the document, and the accuracy of information processing is improved. The method is suitable for document data with different complexities, the automatic auditing process remarkably improves the auditing efficiency, when a large amount of document data is processed, a large amount of time and manpower resources can be saved, the auditing result and the corresponding confidence rate can be output, a user can see the specific result of document analysis, and the auditing reliability and the credibility of the user are improved.

Methods and systems for remote check deposit are disclosed. A check for deposit is processed without the need for a server to receive any image of the check initially. Instead, optical character recognition (OCR) data is received at the server from a mobile device. Verification processing for the check is then performed using the OCR data. If the verification process is successful, a confirmation notification is sent to the mobile device. Subsequently, after sending the confirmation notification, a check image is received, from which the OCR data was determined. The check is, in turn, processed for deposit using the received check image.

The invention discloses a circuit netlist generation method supporting deep learning model multi-stage reasoning, and belongs to the technical field of electronic circuit design and automation, and the method comprises the steps: obtaining a circuit diagram image through optical scanning, carrying out the preprocessing of the image, carrying out the recognition of a component through a deep learning model YOLOv5, and determining the boundary frame coordinates and class information. And further performing port fine identification on the component, and determining the position and the direction of the port. And eliminating character interference by using an optical character recognition technology, performing wire recognition, including Hough straight line detection and jumper processing, and determining starting point and ending point coordinates of the wire. And matching the identification results of the components, the ports and the wires, constructing a topological structure of a circuit diagram, finally generating a circuit netlist, and performing error elimination and JSON format output. According to the method, automatic netlist generation of the analog circuit diagram can be realized, the analog circuit diagram file is converted into the netlist file with device function annotations, and the efficiency and quality of circuit design and simulation are 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 provides a license plate recognition method and system based on deep learning. The method comprises the following steps: carrying out license plate target detection on a preprocessed to-be-recognized vehicle image through a trained YOLOv8 algorithm model; according to the detected license plate area, determining a license plate type and positioning a key point position on the license plate; segmenting the license plate image, and extracting individual license plate character regions; inputting the extracted license plate character area into the trained improved LPRNet network model, and carrying out character recognition; wherein the improved LPRNet network model comprises a cross attention module and a multi-scale feature fusion module, and the cross attention module is used for dynamically fusing semantic information of different levels in shallow and deep feature maps; and the multi-scale feature fusion module is used for carrying out scale fusion on the feature maps of different scales output by the cross attention module, so that the complexity of a license plate recognition model is reduced, and the reliability of license plate recognition in a complex scene is improved.

The invention discloses a fishing boat name identification method and system based on image processing and deep learning. The method comprises the following steps: S1, fishing boat image acquisition; s2, image preprocessing and enhancement are carried out to realize image noise reduction, deblurring and brightness adjustment processing, and image quality judgment is carried out after processing to form closed-loop optimization; s3, self-adaptive image enhancement is carried out to realize illumination compensation and image dynamic enhancement; s4, ship name area detection and segmentation; s5, ship name region feature extraction and optimization; s6, feedback and optimization of an identification result; s7, character recognition and post-processing; s8, performing correction and verification; s9, log identification and report generation; and S10, displaying and storing a result. According to the invention, the fishing boat name identification precision can be effectively 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.

Various embodiments include methods for generating image datasets for training an artificial intelligence machine learning (AI / ML) optical character recognition (OCR) model. Image processing may be performed on a plurality of roadway images to identify street signs within the images and generate a dataset of sign images categorized into sign variants of the same shape, color, pictogram, and characters. An OCR model may process sign images to obtain OCR results for images of each sign variant. An aggregation process may be performed on the OCR results for all sign images within each sign variant to identify a ground truth OCR result for each sign variant. The ground truth OCR result may be used to automatically label all sign images of each sign variant to produce an OCR model training dataset. The produced training dataset may then be used to retrain the initial AI / ML OCR model and / or train other AI / ML OCR models.

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 provides a picture auditing method and device, electronic equipment and a storage medium, and relates to the field of artificial intelligence. The picture auditing method comprises the following steps: performing character detection and recognition on a picture to be audited by utilizing a character recognition module, and extracting text information in the picture to be audited; the text information is matched with a preset text label, a text matching result is determined, and the text matching result is used for indicating whether the text information is successfully matched with the preset text label or not; the to-be-audited picture is matched with a preset image label, an image matching result is determined, and the image matching result is used for indicating whether the to-be-audited picture is successfully matched with the preset image label or not; and according to the text matching result and the image matching result, determining whether the to-be-audited picture is compliant or not. According to the embodiment of the invention, the accuracy of picture auditing can be effectively improved, and the situation of mistaken killing and missing killing can be reduced.

The invention discloses a receipt identification method and device, equipment and a medium, and relates to the technical field of bill content identification. The method comprises the following steps: acquiring a receipt image through a preset acquisition mode, and identifying the receipt image by using a preset optical character identification model to determine text content and corresponding position information in the receipt image; performing information matching on the receipt image in a preset multi-mode large model prompt word bank to obtain a first prompt word, and inputting the first prompt word, the text content and the position information into a preset multi-mode large model to obtain a key information key value pair; and performing information matching on the receipt image in a preset language large model prompt word library to obtain a second prompt word, and inputting the second prompt word, the text content, the position information and the key information key value pair into a preset language large model to obtain a target key information key value pair. Through the technical scheme of the invention, the accuracy of identifying the key field content of the non-standard receipt can be improved.

The invention discloses a contract auditing method and device based on a large model, equipment and a storage medium, and relates to the technical field of artificial intelligence, and the method comprises the steps: analyzing a target contract file based on an optical character recognition technology and a natural language processing technology, and generating a target structured analysis fragment corresponding to the target contract file; constructing a contract relation knowledge graph based on the target structured analysis fragment by using the target large model, and generating a target structured data table; verifying each contract term in the target structured data table based on a dynamic rule engine, and generating an early warning report based on a verification result; performing risk assessment on contract terms in the target structured data table based on the target domain knowledge base, the target large model and the contract relation knowledge graph to generate risk data; and generating a revision suggestion based on the early warning report and the risk data by using the target large model, and revising the target contract file based on the revision suggestion. According to the invention, the automation level and accuracy of contract auditing can be 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 relates to an RAG-based voucher classification method, a medium and equipment, and the method comprises the steps: receiving digital image data of a to-be-classified voucher, carrying out the multi-modal optical character recognition processing to generate a structured OCR result, extracting a text semantic feature vector and a visual layout feature vector based on the structured OCR result, carrying out the fusion of the text semantic feature vector and the visual layout feature vector to generate a multi-modal query vector, and carrying out the classification of the to-be-classified voucher. Similar samples and semantic similarity scores and category metadata thereof are obtained through approximate nearest neighbor retrieval, after an initial candidate category list is generated, key field values are extracted for each candidate category, evidence credibility scores are calculated, comprehensive confidence scores are generated by fusing the semantic similarity scores and the evidence credibility scores, reordering is conducted, and a candidate category list is obtained. And finally, selecting a classification decision path according to the score distribution, and outputting a classification result and an interpretability report. The accuracy and robustness of voucher classification are effectively improved, and complex voucher scenes with changeable formats and fuzzy semantics can be processed; and the interpretability and reliability of the classification decision are enhanced.

The invention discloses an intelligent analysis method and device for an unstructured PDF document, equipment and a medium, and relates to the field of document analysis, the method comprises the steps of obtaining a to-be-analyzed PDF document, analyzing page elements in the PDF document, and generating a document metadata dictionary; if the PDF document does not contain the extractable text, converting the PDF document into an image and performing optical character recognition to generate first structured data; if the PDF document contains the extractable text, judging whether the PDF document contains a table or not; if the PDF document does not contain the table, a PDFMiner is adopted to extract the text, and second structured data is generated; if the PDF document contains the table, performing multi-modal feature extraction and feature fusion on the PDF document according to the document metadata dictionary to obtain multi-modal fusion features, and generating third structured data according to the multi-modal fusion features; according to the method and the device, the analysis precision and efficiency of the PDF document are improved.