684 results about "Text detection" patented technology

A text detail map-based method for supervising end-to-end text detection and recognition, pertaining to the field of text processing. The method comprises the following steps: given an input image containing text in any shape, processing the input image by means of two separate processing branches; designing a text attention head (TAH), and designing a feature pyramid enhancement fusion module (FPEFM); the FPEFM performing feature self-enhancement at different sizes, fusing text image local features and global text position information extracted by a TAH module, and fusing features extracted by the TAH from feature maps of different sizes; stacking a plurality of FPEFMs to continuously enhance the feature representation capability of a model and the depth of the model; and sampling the feature maps to a unified size to obtain a final enhanced feature map.

The invention relates to the technical field of drawing recognition, and discloses an engineering drawing intelligent recognition method and system based on deep learning. The method comprises the steps of performing multi-target cooperative detection on a first processing image based on a detection model, identifying primitive information of the first processing image, and generating a second identification image; positioning a text area of the second recognition image, recognizing a character detection range, determining word tags represented by the character detection range, summarizing character information of the character detection range based on the word tags, and generating third image data; obtaining a correlation degree among the symbols, the attributes and the connecting line information, detecting whether the primitive information accords with a preset rule or not, constructing a symbol topological relation graph, and generating correction information containing a conflict position; and fusing the primitive information, the character information and the correction information to generate structured data comprising a symbol hierarchy tree, an attribute incidence matrix and a conflict label. According to the invention, the efficiency and accuracy of engineering drawing intelligent identification are improved.

The invention discloses a machine generated text detection method, a terminal, a medium and a program product in the field of natural language processing, and the method comprises the steps: inputting a to-be-detected target text into a trained text detection model, and obtaining a detection result outputted by the text detection model; the text detection model comprises a first semantic feature extraction network, a second semantic feature extraction network and a fusion classification network; the first semantic feature extraction network is used for extracting semantic features of keywords in a target text to generate a first representation vector; the second semantic feature extraction network is used for extracting overall semantic features of the target text and generating a second representation vector; the fusion classification network processes the first representation vector and the second representation vector to generate a detection result about whether the target text is a machine-generated text; the training of the text detection model adopts a multi-task learning strategy. According to the method, multi-task training is introduced, so that the accuracy and robustness of machine generated text detection are effectively improved.

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

The invention discloses a natural scene text detection and recognition method and a related device. The method comprises the following steps: acquiring initial image data and a target application scene corresponding to the initial image data; obtaining an initial text detection model from a first pre-training model library and obtaining an initial text recognition model from a second pre-training model library according to the initial image data and the target application scene; training the initial text detection model according to a preset first training data set to obtain a target text detection model, and training the initial text recognition model according to a preset second training data set to obtain a target text recognition model; preprocessing the initial image data to obtain target image data; inputting the target image data into a target text detection model to obtain a target text area; inputting the target text area into a target text recognition model to obtain a first text; and performing result optimization on the first text to obtain a second text. By adopting the method and the device, the accuracy of natural scene text detection and recognition is improved.

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 provides a large language model watermark embedding and detecting method and system based on entropy adaptive adjustment, and belongs to the technical field of large language models. The problems that an existing method is low in low-entropy text detection reliability and affects the text quality are solved. The method comprises the following steps: S1, calculating an entropy value of a token to be generated in real time through an entropy analysis module, and dynamically generating a watermark intensity control parameter; s2, adaptively adjusting a watermark embedding process according to the watermark intensity control parameter, and outputting a watermark text; and S3, calling a weight calculation module, and quantifying the confidence of the watermark signal in combination with the watermark intensity control parameter. The method is mainly used in the field of text source recognition.

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 discloses a scene text detection method and device, and belongs to the field of computers. The method comprises the following steps: performing multi-scale feature extraction on a to-be-detected image by using a multi-scale backbone network module to obtain a multi-scale feature map; constructing an initial feature pyramid based on the multi-scale feature map; using a feature pyramid enhancement network module to perform two-stage feature enhancement processing on the initial feature pyramid to obtain an enhanced feature map, the two stages including a top-down feature enhancement stage and a bottom-up feature enhancement stage, and each stage including a grouping shuffling convolution operation; performing cross-scale feature fusion on the enhanced feature map by using a double attention module to obtain a multi-scale fusion feature map; and performing scene text detection on the multi-scale fusion feature map by using a detection network module to obtain a scene text detection result. By means of the method, the scene text detection effect can be improved, and meanwhile the scene text detection real-time performance is improved.

The invention discloses an incremental multi-language text recognition method and system based on shared knowledge mining, and relates to the technical field of text detection and recognition. Sending the playback set and the data set of the current language into all characteristic recognizers, and mining potential shared characters and shared words among languages based on the prediction consistency of all the characteristic recognizers; based on the shared characters, the shared words and a language domain discriminator, mining a dependency relationship between incremental languages to obtain language probabilities of character levels and word levels, and based on the language probabilities, weighting prediction probability distribution of each characteristic recognizer to obtain character level and word level probability distribution after relationship enhancement; and performing argmax operation on the word level probability distribution after relation enhancement, and selecting an index corresponding to a maximum probability value as a final recognition result. The forgetting of old language knowledge in the incremental learning process can be reduced, so that the continuous learning ability of the text recognizer is improved.

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 relates to the technical field of artificial intelligence, financial science and technology and medical science and technology, and provides a bill information recognition method, device and equipment based on artificial intelligence and a medium. The method comprises the steps that text detection is conducted on a bill image, a text detection box is obtained, and the text detection box and a text corresponding to the text detection box are input into an information extraction model; the method comprises the steps of obtaining key field information, performing seal identification on a bill image to obtain seal content, performing mutual verification on the seal content and the key field information, and if verification succeeds, outputting the key field information. According to the method, the key field information is accurately recognized by combining the position of the text detection box and the text content, then the key field information and the seal content are mutually verified, and the key field information is output under the condition that the key field information and the seal content can be matched. And the effectiveness of the output key field information is improved on the whole.

The invention provides a zero-sample large model text detection method and system based on comparison prompt differential analysis, and the method comprises the steps: S1, coding a to-be-detected text into a lexical element sequence through a word segmentation device, and defining a human style prompt and a large model style prompt; s2, conditional probability calculation: respectively calculating the conditional probability of each lexical element position under two prompt contexts; s3, correction probability difference calculation: calculating a corrected logarithmic probability difference value of the lexical element at each position; s4, summarizing a difference sequence, and recording the difference values of all the lexical elements in the difference sequence in sequence; s5, parameter fitting and score generation: fitting negative exponential function parameters through a difference sequence; and S6, threshold classification judgment: comparing the fitted parameters with a preset threshold, and judging that the text is a text generated by a large language model or written by human. Based on the technical scheme of the invention, the detection can be completed without any training or fine tuning; the interpretability is high, and the word-level visualization capability is achieved; and the robustness is good and the fault tolerance is high.

Embodiments of the invention disclose a text detection method and apparatus, an electronic device and a storage medium. The method comprises the steps of obtaining a target scene image; determining an image entropy value and image texture complexity of the target scene image; obtaining target recognition precision and target recognition efficiency of the target scene image; determining n convolution layers and m textbox layers in a target neural network according to the image entropy, the image texture complexity, the target recognition precision and the target recognition efficiency; performing convolution on the target scene image to obtain n feature maps; acquiring m feature maps from the n feature maps; determining m initial frame sets of the m feature maps according to the target recognition precision, the target recognition efficiency and the scale corresponding to each feature map in the m feature maps; determining m first texts and m first backgrounds of the m feature maps according to the m initial frame sets; and determining a target text according to the m first texts, the m first backgrounds and the scale corresponding to each feature map in the m feature maps.

The invention discloses an industrial defect image-text joint detection method and related equipment. The method comprises the following steps: acquiring an industrial product image and extracting multi-scale visual features; obtaining text information related to the image and extracting text features; the image and text features are input into a large language model with a cross-modal attention mechanism for fusion reasoning, and defect semantic features are generated; outputting a pixel-level defect mask through a segmentation decoding module based on the features; and finally, jointly generating defect text description and superposing the defect text description with the original image to form an image-text detection result to be output. The method makes full use of complementarity of visual perception and text knowledge, has the advantages of low sample dependence, high robustness and human readability of output results, is suitable for quality monitoring and defect analysis scenes in the industrial manufacturing process, and has good practicability and popularization value.

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 a bimodal industrial package defect detection method, and belongs to the technical field of industrial package label quality detection, and the method comprises the steps: collecting a label electronic design drawing and a label object image on an industrial package; respectively separating the label physical image and the label electronic design drawing into a text image and a label area image; identifying a text region in the text image by adopting an enhanced real-time scene text detection network; recognizing a text region by adopting a scene text recognition model, and detecting text defects; detecting label area image defects based on a gray scale matching method; and fusing the text image and the label area image to the original position, and marking the positions of the image defect and the text defect. According to the invention, the efficiency and precision of label defect detection are improved, the adaptability and robustness of the algorithm are significantly enhanced through the adaptive image acquisition module and the bimodal detection method, the requirements of high precision and high efficiency of modern industrial production are met, and the method has a wide application prospect.

The invention belongs to the field of smart power grids and computer vision, and particularly relates to an improved SwinTextSpotter v2-based robust generalization detection and identification method for a power station wiring image text. Comprising the following steps of 1, sending an input image into a text detection and recognition network based on multi-modal learning for training and prediction, extracting a backbone network through shared features to obtain a shared feature map, and further sending the shared feature map into a text detection module to obtain a text detection result and a text feature map; 2, sending the text feature map into a visual feature extraction and prediction module to obtain a feature sequence, and then matching the predicted feature sequence with the standard representation obtained by the character structure feature extraction and prediction module to obtain an identification result; and the like. According to the method, the detection and recognition precision of the model on irregular texts and Chinese character texts is improved robustly, and the generalization performance of detection and recognition on various types of wiring texts and texts is improved.

The invention discloses a text detection method and device, and relates to the technical field of data processing, and the method comprises the steps: carrying out the corner detection of text data of an original image based on a Harris corner detection algorithm, and obtaining a corner detection image; performing weighted fusion on the corner detection image and the original image based on a preset transparent parameter to obtain a fused image; performing feature extraction on the fused image based on a preset convolutional neural network model and a Vision Transform network model, and mapping the extracted features to a character probability based on a linear classifier and a softmax function; and generating extracted text data based on the character probability of each extracted feature. Compared with the prior art, corner detection is carried out on the text data of the original image based on the Harris corner detection algorithm to obtain the corner detection graph, even if the text data is rich and diversified in shape, the Harris corner detection algorithm can still carry out accurate positioning and extraction on key corners in the original image, and the accuracy of the corner detection graph is improved. Therefore, the method has better adaptive capacity for detecting the text data with abundant and diversified shapes.

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.

The invention provides a table document recognition method and device, electronic equipment and a storage medium, and the method comprises the steps: carrying out the table detection of a to-be-recognized document image, and obtaining a table image; performing table line detection on the table image to obtain a transverse line segment and a vertical line segment; determining table cell intersection point information based on the transverse line segments and the vertical line segments, and determining table cell structure information according to the intersection point information; performing text detection on the to-be-recognized document image to obtain a detection result; and matching each text detection box in the detection result with each cell in the table cell structure information to obtain a text line region corresponding to each cell, and performing text recognition on the text line region to obtain a text recognition result of each cell. According to the table structure identification method and device, table line detection is carried out on the table image, the transverse line segments and the vertical line segments are obtained according to detection, the intersection point information and the structure information of the table cells can be accurately determined, and the accuracy of table structure identification is improved.

The invention relates to a man-machine mixed text detection method based on context expansion, which expands the detection range from a single target sentence to the context of the sentence by introducing a sliding window mechanism, and designs a method for weakening context noise interference to improve the accuracy and generalization of model prediction. The method comprises the following steps: firstly, collecting texts with the window length as a training set and a verification set, and extracting semantic features and coherence features for training to obtain a trained detection model; a sliding window mechanism is applied to a test set to detect all window texts, and a small sliding window step length is set, so that sentences in the window texts can obtain a plurality of prediction probabilities under different context conditions. And adopting a detection mechanism based on confidence to perform confidence weighting on the plurality of prediction probabilities to obtain a final probability that the sentence is the AI text.

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.

There is a need for more effective and efficient anomalous text detection. This need can be addressed by, for example, solutions for anomalous text detection that include the steps of performing a group of exploration-exploitation keyword extraction iterations based at least in part on one or more training corpus data entries until a per-iteration keyword list for an ultimate exploration-exploitation keyword extraction iteration satisfies a keyword list threshold condition; and subsequent to performing the exploration-exploitation keyword extraction iterations: processing one or more input corpus data entries using the language-model-based binary classification model to generate one or more inferred anomaly probabilities, processing the one or more input corpus data entries using the keyword model to generate explanatory metadata for the one or more inferred anomaly probabilities, and performing one or more prediction-based actions based at least in part on the one or more inferred anomaly probabilities and the explanatory metadata.

The invention discloses an ancient book text detection method based on a joint enhanced feature pyramid network, and the method comprises the steps: collecting an original image and a corresponding textbox label from an ancient book document image detection data set, and dividing the original image and the corresponding textbox label into a training set and a test set; constructing a joint enhanced feature pyramid network, wherein the joint enhanced feature pyramid network comprises a character structure extraction module and a dual-path fusion module; an ancient book text detection network model is constructed based on the joint enhanced feature pyramid network, the ancient book text detection network model comprises an encoder, a decoder, a classification head and a detection head, and the joint enhanced feature pyramid network is adopted in the decoder; inputting the preprocessed ancient book document images in the training set into an ancient book text detection network model for training; and inputting ancient book document images in the test set into the trained ancient book text detection network model, generating character box position information, and performing character cutting for evaluation. According to the invention, high-precision ancient book text detection is realized.

The present invention relates to the field of image processing technology, and specifically to an invoice information positioning and interception system and method based on image recognition, including a data processing unit, a recognition, matching and extraction unit, an information integration and verification unit, a data storage and management unit and a security assurance unit. In the present invention, the processed image text and pattern are clarified, which can improve the accuracy of text positioning by the text detection and positioning module, thereby reducing the dependence on image quality. After the characters are recognized by the recognition, matching and extraction unit, the format is adapted by the format adaptation module, and then the information is checked and quickly extracted by the verification and extraction module, so that invoices of different types and formats can be quickly recognized, matched and extracted, reducing the interference of font and symbol recognition limitations and the diversity of invoice formats, ensuring the accuracy of invoice information recognition and positioning, and ensuring the integrity and correctness of information extraction.