380 results about "Text detection" patented technology

The invention discloses a detection method for printing effect verification, and the method comprises the steps: collecting scanning image information and environment sensing information, employing an improved OTSU algorithm and an illumination compensation module to process the scanning image information, and extracting a standardized image feature matrix containing the characteristics of character integrity, edge sharpness and the like; identifying a defect mode by using an EAST text detection algorithm and morphological operation, and generating a quantitative evaluation vector; the vector and environment sensing information are subjected to space-time alignment, a printing quality degradation model is constructed through a density peak value clustering algorithm, and a multi-dimensional quality index set is established; and finally, based on the graph attention network and the time sequence convolutional network, analyzing the incidence relation between the printing quality and the equipment state, and outputting a verification report containing a quality score, a defect positioning graph and a life prediction curve. According to the method, accurate quantitative evaluation of the printing quality is realized, a dynamic association model of the quality, the environmental parameters and the equipment aging is established, and an intelligent decision basis is provided for printing quality maintenance.

The invention discloses a multi-modal fusion bank receipt intelligent processing system and method based on vision and NLP, and the method comprises the steps: receiving a bank receipt picture or a PDF document, and completing the text detection, direction correction and character recognition through a visual processing engine; a three-level receipt independent segmentation mechanism is applied, and independent receipt records are divided through spatial clustering analysis, semantic analysis, visual verification and cross-page association processing; jointly extracting text features and layout features of each receipt through a double-flow multi-modal fusion model, and fusing the text features and the layout features; field-level data extraction is executed through a field extraction engine, and financial data verification including account number, amount, date and abnormity quadruple verification is carried out; and generating structured JSON output to obtain a bank receipt processing result. According to the invention, the innovative five-layer processing architecture realizes high-precision analysis of the bank receipts through an independently researched and developed receipt independent segmentation engine, a vision-semantic fusion model and a financial data verification system.

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 belongs to the technical field of video live broadcast monitoring management, and particularly relates to a live broadcast content real-time compliance detection method and system based on deep learning, and the method breaks through the limitation that traditional text detection only depends on dominant keywords by constructing a text semantic analysis chain matched with original words and mapping sensitive words through constructing metaphor words. The identification of deep semantic violation contents such as metaphor and private obscure expression is realized; the method comprises the following steps: performing linkage verification on text violation clues and image behavior characteristics through behavior characteristic analysis of image frames before and after time positioning association of text violation based on a time positioning cross-modal collaborative detection mechanism, and forming a multi-modal judgment logic for text triggering image confirmation; a progressive detection strategy of hierarchical thresholds is designed, a first threshold is set as violation confirmation and a second threshold is set as suspected violation early warning, and real-time preliminary detection and precise secondary verification are combined, so that stepped processing from suspected violation to violation confirmation is realized, and the real-time performance and accuracy of detection are balanced.

The invention provides a form text boundary adaptive information extraction method and system in the technical field of computer information processing. The method comprises the following steps: S1, identifying a title frame from a form image; s2, calculating the aspect ratio of the title frame to determine the title arrangement direction of the table titles; s3, detecting a text detection box of the table image, and performing classification prediction of a text arrangement direction on the text detection box by combining a text direction model with a title arrangement direction; s4, controlling affine transformation of the table image through the text arrangement direction and the text detection box to obtain a corrected image; s5, identifying a table title in the corrected image, and matching a table type from the table type configuration file based on the table title; s6, extracting keywords from the corrected image based on the table type; and S7, processing the keyword to extract table information. The method has the advantages that the adaptability, accuracy and efficiency of table information extraction are greatly improved.

The invention discloses an OCR-based medical document intelligent identification method and system, and relates to the technical field of document identification, and the method comprises the steps: collecting a medical document image through a mobile terminal, generating a binary image based on the collected image through U-Net in combination with multi-scale feature fusion and an attention mechanism, and cutting the binary image; based on the cut binary image, text information is extracted through an OCR model, and a structured field is extracted according to the typesetting rule and geometric distribution of the medical document; and performing deterministic rule judgment and risk assessment on the structured data. According to the method, the image calculation complexity is reduced through standard graying processing, the text region feature extraction precision is improved by fusing a U-Net structure of a CBAM attention mechanism, effective fusion and noise suppression of multi-scale features are realized in combination with Attention Gate, text direction correction is realized in combination with Hough transform, and the text detection robustness and recognition accuracy are improved.

The invention provides a PDF (Portable Document Format) optimized translation method based on intelligent text detection, which comprises the following steps of: 1, analyzing a PDF document, respectively identifying a text region and an image region in the PDF document, and extracting a native text block of the text region in the PDF document and structured position information of the native text block; the recognition result and the native text block are combined into a unified structured data set; 3, performing machine translation by adopting a window translation strategy in combination with the context, and extracting a target translation; adjacent text blocks are dynamically associated during translation, and a target text range is defined through boundary markers; and 4, dynamically optimizing the text layout according to the target translation length, and backfilling a translation result to an original position. According to the method, the translated text can be accurately and seamlessly backfilled to the corresponding position of the original image, meanwhile, the content of the original image is ensured not to be shielded, and visual disorder or information loss caused by text coverage is avoided.

The invention provides a false news detection method and device and terminal equipment, and is suitable for the technical field of data processing.The method comprises the steps that statement segmentation and invalid statement filtering processing are conducted on news text information to be detected, and multiple pieces of news statement information to be detected are obtained; generating multiple pieces of to-be-detected news text structured information according to the to-be-detected news text information, the multiple pieces of to-be-detected news statement information, preset to-be-detected news text extraction guide information and a preset to-be-detected news text structured information extraction model; and generating multiple pieces of news text detection information according to the to-be-detected news text information, the multiple pieces of to-be-detected news text structured information and a preset news text detection model. According to the method, the requirements of scenes such as manual review and judicial evidence collection on interpretability and traceability are met, and the accuracy, reliability and engineering reproducibility of false news detection in a complex context are remarkably improved.

The invention discloses an archive information extraction and intelligent management system based on self-supervised learning, and the system comprises the following modules: a collection preprocessing module which is used for collecting original data of an archive and generating a layout sample data set and an original text mapping table; the self-supervision pre-training module is used for obtaining a three-mode unified encoder model; the document analysis module is used for text detection, character recognition and table structure recovery and generating a document analysis data set; the information extraction module is used for aligning pointer positioning and optimal transmission, outputting metadata and a relational data set and registering evidence entries; the alignment warehousing module is used for generating a standardized record library and an evidence link table based on the structure enhanced double-tower vector recall; and the strategy operation and maintenance module is used for authority control, desensitization, archiving and incremental updating. According to the invention, through combination of three-mode self-supervision and an evidence chain, accurate extraction, aligned warehousing and traceable management of archive elements are realized.

This invention relates to the field of image recognition technology, and more particularly to a general-purpose structured document recognition method and system based on deep learning. The method includes: acquiring document image information and preprocessing the acquired images; inputting a standardized document image into a text detection network to locate text instances in the image and saving the location information to a file; extracting text images from the document image based on the detected text instance location coordinates and inputting them into a text recognition network for recognition, saving the recognition result after the location information; using a key text extraction network to classify the text entities in the recognition result, removing non-key types, and then saving the classification result after the recognition result; and performing structured processing on the key text extraction result and displaying it. This invention can extract key text information from complex document images, enabling intelligent document reading, and is applicable to various types of documents.

The invention discloses an automatic wrong question collection method based on video image collection, and belongs to the technical field of image processing, and the method specifically comprises the steps: obtaining continuous video frames of homework or test paper through video collection equipment, and extracting a question region image through preprocessing; executing text detection and recognition on the subject area image, extracting subject content and answer content, and constructing a data pair; matching the data pair with a standard answer library, and identifying a wrong question area in combination with correction trace features; performing knowledge point labeling and difficulty grading on the regional questions, performing classification and collection based on labeling results, and generating and outputting a structured wrong question set; according to the method, automatic identification, classification and collection of the wrong questions are realized, the wrong question sorting efficiency is improved, and the method is suitable for wrong question management scenes of various homework and test papers.

The invention relates to the technical field of artificial intelligence, and discloses a paper text processing method based on intelligent glasses. The method comprises the steps that intelligent glasses are awakened through an awakening word, a user can conduct image recognition through a voice instruction, and whether a textbox is complete or not is judged. If the textbox is complete, character recognition is carried out; if not, the intelligent glasses help the user to adjust through voice guidance until the textbox is completely displayed. If the recognized characters are not the language set by the user, the intelligent glasses automatically translate the recognized characters into the user language, and voice synthesis is carried out to generate a voice file. And after generation, inquiring the user whether to play the voice, and if not, encrypting and storing the voice file. The problems that current text detection precision is not high, the reading process is not natural, user control is complex, and a feedback mechanism is single are solved.

The invention provides an engineering drawing steel bar bulk sample annotation text detection method, system and device based on deep learning, and a storage medium. The method comprises the following steps: S1, preprocessing an engineering drawing image and labeling a steel bar bulk sample annotation text region data set; s2, constructing a staged multi-scale feature map extraction backbone network framework; s3, introducing compression excitation into each scale feature map; s4, adopting an ACON adaptive activation function in nonlinear modeling; s5, constructing a multi-scale feature pyramid structure based on the RSE-FPN, and superposing scales to reinforce fusion; and S6, outputting the approximate binary image to realize accurate prediction of the bounding box. According to the method, the OCR detection precision is remarkably improved, small character and complex background interference is effectively overcome, and missing detection and false detection are reduced; the model is lightweight to facilitate efficient deployment of equipment; the output boundary is compact and accurate, the semantics is reasonable, and subsequent recognition and analysis are facilitated; the method has excellent generalization ability, adapts to steel bar large sample drawings of different formats, definitions and styles, and is high in engineering practicability.

The invention belongs to the technical field of computer vision and natural language processing, and provides a universal table recognition method and system based on multi-modal feature fusion, and the method comprises the steps: carrying out the preprocessing of a table image, obtaining a preprocessed table image, carrying out the image feature extraction of the preprocessed table image, and obtaining an image feature vector; performing text detection and text recognition on the preprocessed table image to obtain text contents of all text blocks in the image, and performing feature extraction on the text contents to obtain a text feature vector; performing multi-modal fusion on the extracted image feature vector and the text feature vector to obtain a feature vector represented by a multi-modal fusion feature; and performing set membership judgment on the feature vector represented by the multi-modal fusion feature through a preset set membership classifier, and performing visual display. According to the method, the image features and the text features are deeply combined, so that efficient and accurate recognition of a complex table structure is realized.

The invention provides a table restoration method and device based on optical character recognition, which are used for respectively carrying out structure recognition on rows and columns of a table and recognizing texts in the same row by utilizing text detection, so that a row textbox is recognized more accurately, and more accurate table structure restoration is realized. Comprising the steps that firstly, textbox detection is conducted on input data to obtain a scanning result, the input data can specifically comprise images or other unreadable documents and the like, the input data comprises an input table, and the scanning result comprises information of at least one textbox in the input table; the information comprises coordinates, length, width or area of a textbox; performing text detection on the at least one textbox to determine a row textbox, and determining a column textbox according to the at least one textbox; the row textbox and the column textbox are then merged to obtain an output table, which is generally a readable table.

Systems and methods for detecting text in videos. To address problems with conventional Optical Character Recognition (OCR) systems, the present disclosure provides detection of text for improved OCR. Aspects of the present disclosure can, therefore, be utilized to detect a textual logo in videos, including when the text of the textual logo is clearly visible and when the text is inferred. Thus, examples capture appearance time of a textual logo from a video view perspective. Aspects use a multi-threshold pipeline for detecting video frames including the textual logo. A textual-visual scoring system is additionally used to leverage visual aspects of text in logos. A shot detection system is used to detect inferred text beyond a detected video frame. One or more verification models can be further applied.

The invention relates to the technical field of natural language processing, in particular to a project establishment repeatability detection method and device based on ocean engineering scientific research projects. According to the method, word segmentation processing is carried out on historical scientific research projects by constructing an ocean engineering terminology dictionary, and meanwhile, independent processing is carried out on numerical parameters, so that the problems of low terminology recognition accuracy, misrecognition and the like are solved; when the text similarity is detected, semantic similarity compensation is introduced, invisible correlation is effectively recognized, and the matching limitation is broken through; in addition, the similarity of item attributes is considered, repeatability detection is comprehensively carried out based on text similarity and attribute similarity, and the defects of plain text detection are effectively overcome.

The invention discloses a bent and rotated text detection and recognition method, and relates to the technical field of computer vision. The method comprises the steps that firstly, an input image is processed through a multi-view fusion detection mechanism, and an optimal detection view image is generated and screened out to eliminate large-angle rotation interference; secondly, on the basis of the optimal view angle image, parameterized geometric modeling is carried out on a text bending boundary by adopting a segmented cubic Hermite interpolation curve, and a smooth and continuous text contour curve is constructed; then, a Hermite curve-based feature alignment unit is utilized to carry out adaptive sampling and mapping on image features according to the contour curve, and a curved text region is converted into a regular rectangular feature map so as to eliminate geometric deformation; and finally, inputting the rule feature map into the text recognition model, and outputting a recognition result. According to the method, the detection and recognition precision and robustness of the bent and rotating text in a complex industrial scene are greatly improved.

The invention discloses a handwritten medical record OCR recognition system and method based on deep learning. The system comprises an image acquisition and preprocessing module, a text detection and region positioning module, a handwritten character recognition module, a semantic correction module and a result storage and export module. The image acquisition and preprocessing module is used for receiving the original medical record image and performing image quality improvement processing to obtain optimized image data; the text detection and region positioning module is used for detecting a text region from the optimized image data and positioning the position information of the text region to obtain text region data; the handwritten character recognition module is used for performing recognition processing on a character sequence in the text region data to obtain an initial text recognition result; the semantic correction module is used for processing the initial text recognition result based on medical priori knowledge to obtain a corrected text recognition result; and the result storage and export module is used for outputting the corrected text recognition result to a target system in a structured format.

The invention provides an image-text detection model training method and device, electronic equipment and a storage medium, relates to the technical field of data processing, in particular to the fields of computer vision, natural language processing and the like, and can be used for application scenes such as harmful image-text detection and classification. According to the specific scheme, according to a thinking chain data set, a visual agent, a text agent and a fusion agent in a to-be-trained basic model are trained, and the basic model is obtained; inputting the original data set into the basic model, screening an output initial result through a triple confidence threshold, and constructing a deep thinking training data set; performing data simplification processing on the deep thinking training data set to generate a quick answer training data set; and using the deep thinking training data set and the fast answer training data set to train a deep thinking channel and a fast answer channel of the to-be-trained image-text detection model in parallel to obtain the image-text detection model. According to the scheme, the detection capability of harmful image-text contents can be improved.

The invention discloses a multi-modal content real-time detection and dynamic replacement method and system. The method comprises the following steps: buffering an input audio / video stream for a fixed time length and synchronously decomposing the input audio / video stream into a video image, an audio text and a picture text; performing sensitive content detection on the three types of data in parallel, and judging overall violation according to at least one violation; and according to the violation type, selecting a global replacement mode or a local mask mode to carry out real-time replacement processing. The system comprises corresponding function modules. According to the invention, multi-modal decomposition and parallel detection are combined with cross-modal cooperative determination, so that the problem of high missed determination rate of single detection is solved; the detection precision and the real-time performance are balanced through fixed buffering and dynamic replacement strategies; particularly, a text detection and recognition fusion model is adopted to improve the dynamic subtitle recognition rate; wide-temperature hardware and fan-free design are combined, stable operation of the system in severe environments such as outdoors is guaranteed, and accuracy, real-time performance and reliability of content security control are remarkably improved.

A method for detecting text in an image includes receiving an image including text; receiving a command including a text detection condition; and inputting the image and the command into a text detection model so as to generate a sequence indicating a detection result of a text instance included in the image according to the text detection condition.

The invention discloses an intelligent detection method and system for AI texts, and relates to the technical field of AI text detection.The method comprises the steps that AI texts and non-AI texts corresponding to multiple themes are obtained, and a training set of theme contrast is constructed; performing word segmentation processing on each text in the training set, and quantifying vocabulary features of each text; analyzing each sentence in each text in the training set, and quantifying syntactic features of each text; on the basis of the vocabulary features and syntactic features of all the texts, the style feature distinction degree of each theme is calculated; calculating the text weight of each subject based on the style feature distinction degree, constructing a dichotomy model, and improving a loss function of the model in combination with the text weight; and training the model based on the improved loss function and the training set, and realizing intelligent detection of the AI text through the trained dichotomy model. According to the method, excessive dependence of the model on specific content vocabularies can be effectively avoided, and adaptability and discrimination accuracy of the model to different topic texts are improved.

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

This invention relates to a method, system, device, and medium for recognizing financial forms. The method includes: acquiring a financial form to be recognized; extracting feature maps of different scales from the financial form using a segmentation-based text detection algorithm and fusing them; identifying and extracting text regions from the fused feature maps; extracting text sequence features from the text regions and transcribing the extracted text sequence features based on an attention mechanism to extract text sequences from the text regions; encoding and semantically enhancing the text sequences to obtain a comprehensive vector sequence; and decoding the comprehensive vector sequence to recognize named entities in the financial form. This invention achieves automated, refined extraction and structured processing of financial form information.

The application provides a nameplate VIN code recognition method and device, a storage medium and equipment. In the method, a character detection model is constructed, and the character detection model is reinforced by an auxiliary branch network. Meanwhile, a plurality of sample pictures with specified labels are used as training samples to construct a character recognition model. In this way, for a to-be-recognized automobile nameplate image, the position of the VIN code region is detected based on the character detection model, and the image is cropped and then input into the text recognition model, and then the target VIN code contained in the automobile nameplate image is recognized. In this way, the detection capability of the model for the VIN code is enhanced by using the auxiliary learning mode, so that the recognition accuracy of the nameplate VIN code is improved.

The present disclosure provides a text detection method and device, electronic equipment and storage medium, relates to the field of artificial intelligence, in particular to the field of natural language processing. The specific implementation scheme is: performing keyword matching on the to-be-detected text to obtain a matching result; in response to the matching result representing that the to-be-detected text does not include a keyword corresponding to negative information, preprocessing the to-be-detected text to obtain a candidate to-be-detected text; replacing an entity included in the candidate to-be-detected text with a target entity based on a knowledge graph corresponding to the to-be-detected text to obtain at least one target detection text; and detecting whether the target detection text includes the negative information. By applying the technical solution of the present disclosure, the comprehensiveness and accuracy of negative information detection can be improved.

The invention discloses a text detection method and electronic equipment, and relates to the technical field of natural language processing, and the method comprises the following steps: in response to a detection operation for a target text, carrying out syntactic analysis on the target text to obtain part-of-speech information and dependency syntax information of the target text; then, inputting part-of-speech information and dependency syntax information of the target text into the text detection model to obtain an initial revised version of the target text; and then, verifying the initial revised version of the target text to obtain a target revised version of the target text. In the embodiment of the invention, accurate detection of the text can be realized, so that the text quality is improved.

The invention discloses a multilevel harmful text classification method, and relates to the technical field of natural language processing and text classification, and the method comprises the steps: firstly collecting harmful and normal texts to construct a positive and negative sample library; establishing a harmful category hierarchical tree; blocking the seed data, generating harmful examples by using a cue word template, and screening the harmful examples; then, a model is built for comparative learning training; finally, dynamic threshold adjustment is performed, a threshold is determined by combining real-time data, and whether the sample belongs to a specific harmful category or not is judged accordingly. Through a dynamic harmful category hierarchical tree and hierarchical activeness matching algorithm, it is ensured that a classification system is comprehensive in timeliness and accurate in positioning; meanwhile, seed text partitioning, sample expansion and the like are utilized to generate high-quality training data and flexibly adjust a classification threshold, so that the accuracy, adaptability and practicability of harmful text detection are remarkably improved.

The present disclosure relates to the technical field of image processing, and provides a text detection method and device, a storage medium and an electronic device. The text detection method comprises: acquiring a to-be-detected image containing to-be-detected text; inputting the to-be-detected image into a pre-trained text region detection model to obtain a target text region output by the text region detection model, wherein the text region detection model is determined from at least two student models, the text region detection model comprises a backbone network layer, a feature fusion layer and an output layer, the backbone network layer is used for feature extraction on the to-be-detected image to obtain a feature vector, the feature fusion network is used for fusion on the feature vector to obtain a fusion result, and the output layer is used for outputting the target text region according to the fusion result; and performing text recognition on the target text region in the to-be-detected image to obtain a text detection result.