116 results about "Handwriting recognition" patented technology

The invention discloses a subjective question scoring and evaluation system based on SOLO classification, and belongs to the field of machine paper marking. In order to solve the defect that the thinking structure level of students cannot be reflected due to the fact that only attention scores are concerned in traditional machine paper marking of subjective questions, an SOLO classification theory is introduced, keyword-logic-thinking hierarchy mapping is constructed through multiple rounds of cue word engineering and directional fine adjustment in combination with a large language model, whole-section semantic analysis and hierarchical score assignment are achieved, and the thinking structure level of the students cannot be reflected. Generating a traceable report; oCR handwriting recognition is fused to form a closed-loop automatic system to adapt to an offline scene; custom configuration of teachers is supported, multi-dimensional learning condition visualization data is generated, and teaching is assisted. The method is applied to the field of intelligent education and has the advantages of automation, flexibility and the like.

The invention relates to the technical field of online education, and discloses an online teaching system, which comprises an intelligent handwriting recognition module used for capturing a writing track of a user, detecting a stroke sequence, force, an angle and a writing sequence and analyzing writing content, and the intelligent handwriting recognition module is used for recognizing the writing content through an advanced sensor and an algorithm. The stroke sequence error detection module captures a writing track of a user in real time, analyzes multiple items of information such as stroke sequence, force and angle, sets a writing path of the user as a vector, and calculates an included angle between strokes by using a track analysis algorithm, so that stroke sequence errors are accurately recognized, and the accuracy of error detection is improved. According to the method, the writing process can feed back the defects in the writing process in time, the user is helped to continuously improve in the learning process, and then the error detection and recording module not only can automatically recognize errors occurring in the writing process of the user, but also can record the error parts in detail.

The invention discloses an offline writer identification method and device, electronic equipment and a medium. The method comprises the following steps: acquiring handwritten word image data; converting the handwritten image into a grayscale image, performing binarization processing on the grayscale image, adjusting the size of the processed image to a preset size, and constructing a training set; constructing a handwriting recognition model, and training the handwriting recognition model by adopting the training set; wherein the handwriting recognition model comprises a local modeling module, an identical branch and a global module, the local modeling module is used for capturing local features, the identical branch is used for extracting global features, and the global module is used for integrating the local features and the global features; and inputting a handwritten image to be recognized into the trained handwriting recognition model to obtain a result of a final classification layer of the model, performing softmax probability calculation, and taking a writer with the maximum probability as a recognition result. According to the method, robust feature representation can be effectively extracted from limited handwriting, and the recognition precision far higher than that of other methods is achieved.

This invention relates to systems and methods for real-time dynamic classification and orchestration of test automation components in distributed DevOps environments. The system features an Auto Identify Automation (AIA) engine that leverages supervised learning, Multi-Modal Artificial Intelligence (AI), and Generative AI technologies. It includes a Smart Scenario Designer interface that allows users to author test scenarios using handwriting and voice inputs, which are processed in real-time by AI-driven handwriting recognition, voice recognition, and Natural Language Processing (NLP). The system dynamically suggests relevant automated components via a smart bubble pane, facilitating rapid scenario creation. The architecture is tool-agnostic and scalable, with a Shared Workbench Engine that supports real-time collaboration and conflict resolution. The system continuously adapts and improves, ensuring that the automation suite remains consistent, up-to-date, and aligned with evolving software requirements, enabling efficient and user-friendly management of complex test automation processes.

The invention discloses a mathematical formula handwriting recognition method and system, and the method comprises the steps: carrying out the feature extraction of a low level, a middle level and a high level of obtained data of a handwritten mathematical formula, extracting detail parameters of the formula, such as stroke attributes, a space structure, an overall structure and a logic relation, and carrying out the formula conversion of an extraction result, and a high-precision handwritten mathematical formula identification result can be obtained. According to the method, hierarchical feature extraction is performed on the handwritten mathematical formula data, and the extracted detail parameters are taken as the identification basis of the complex mathematical formula, so that the identification accuracy of the complex handwritten mathematical formula can be improved, and the identification level of the complex handwritten mathematical formula can be effectively improved.

The invention relates to a performance-programmable pressure sensor and a preparation method and application thereof.The preparation method of the performance-programmable pressure sensor comprises the steps that a PDMS soft template is prepared firstly, then an electrode layer base layer and an ion layer are prepared based on the PDMS soft template, a conductive film is deposited on the surface of the electrode layer base layer to obtain an electrode layer, and the electrode layer base layer and the ion layer are prepared. And finally, assembling the two electrode layers and one ion layer to prepare the pressure sensor with programmable performance. According to the structural design of the pressure sensor with the programmable performance, a gradient modulus and microstructure comprehensive strategy is provided, microstructures with different heights and rigid-flexible mixed materials are adopted to regulate and control the sensitivity and the detection range of the sensing device, and high-sensitivity sensing in a wide pressure range is achieved; meanwhile, the pressure sensor can adapt to a handwriting recognition application scene, the sensitivity or the sensing range can be improved, and accurate information is extracted and recognized through a machine learning model.

A method for providing recommendations based on handwritten data in a handwritten document is disclosed. The method includes fetching the handwritten data corresponding to strokes made by a user in the handwritten document on a screen of an electronic device. The method also includes recognizing text associated with the fetched handwritten data by employing a handwriting recognition technique. The method further includes identifying keywords in the recognized text and determining recommendations based on the identified keywords using a named entity linking and / or a knowledge graph. The recommendations include handwritten documents stored in a database and / or weblinks. The method also includes ranking the determined recommendations based on a similarity score of each of the determined one or more recommendations with respect to the identified one or more keywords and rendering the ranked recommendations to the user.

The invention relates to a design method of an intelligent document indication copying and clearing system based on artificial intelligence, and belongs to the field of computer software. According to the handwritten endorsement content recognition method, a handwritten recognition algorithm model is established for handwritten endorsement content recognition, firstly, the handwritten endorsement area image is obtained, input is provided for handwritten content recognition, the handwritten endorsement content is recognized and extracted, manual extraction and input work is avoided, and the difficulty and measurement of manual recognition work are reduced. In order to improve the accuracy of content finally obtained by handwritten instructions, a content error correction link is added, a content error correction algorithm model is established, error detection is carried out from the two aspects of word granularity and word granularity through word segmentation, an optimal correction word is obtained through a language model, error correction is completed, and the final accuracy of handwritten content recognition is greatly improved. And through a template engine technology, the document is automatically generated according to the constructed copying and clearing template, so that the work of manually sorting and inputting the document is omitted, and the document circulation speed is greatly improved.

The invention discloses a Mongolian handwritten text recognition method based on improved OfficientNet, a generative adversarial network is utilized to generate a Mongolian handwritten text image with a human handwriting style as a data set, model optimization is performed according to the characteristics that Mongolian handwritten text characters are easy to distort, deform and the like, targeted preprocessing and data expansion are performed on data, and the recognition accuracy of the Mongolian handwritten text image is improved. The diversity of the data set is improved; a backbone network of a feature extraction module of the recognition model is an improved OfficientNet network, and an SE module in an MBConv structure of the OfficientNet network is replaced with an ECA module; and training the recognition model by using the data set, and performing Mongolian handwriting recognition by using the trained recognition model. According to the recognition model, the recognition precision and efficiency can be optimized under limited resources.

A handwriting recognition method and apparatus, and a computer-readable storage medium. The handwriting recognition method comprises: acquiring text image data and text track data, the text image data and the text track data being used to recognize same handwriting content to be recognized; obtaining a first text feature on the basis of the text image data, and obtaining a second text feature on the basis of the text track data; and obtaining a recognition result on the basis of the first text feature and the second text feature.

The invention relates to the technical field of teaching, and discloses a handwriting recognition method, which comprises the following steps that S1, Chinese character writing specifications are dynamically displayed through AR, a user participates in practice in real time, and the practice is analyzed in real time; s2, a multi-sensory collaborative teaching practice module which synchronously integrates visual, auditory and tactile feedbacks; s3, forming a review strategy based on an intelligent memory enhancement mechanism of the user behavior data; s4, gamifying an interactive interface, and supporting real-time stroke detection and error correction; and S5, a personalized learning path generation module adapted to the learning progress and error mode of the user. According to the method, the writing track of the user is analyzed in real time through the double-branch convolutional neural network, three-level error classification is output, and AR dynamic feedback is combined, so that the stroke sequence detection rate is 92%, the font analysis delay is less than 20 ms, and the error correction efficiency is improved; the multi-sensory collaboration module dynamically distributes visual, auditory and tactile weights by using an attention mechanism to adapt to different learning preferences, so that the memory retention rate is improved, and the environment adaptive capacity is enhanced.

The invention discloses a handwriting recognition and judgment method and system based on knowledge transfer learning, and relates to the field of handwriting recognition. Establishing a rectangular plane coordinate system, calculating a curvature and an average curvature, comparing the curvature and the average curvature with a preset threshold value, judging stroke types, and counting linear strokes and the total stroke number; calculating a vector included angle and an angle average value; the total handwriting length, the average acceleration and the handwriting generation rate are obtained in combination with real handwriting sample data, and then a comprehensive index is calculated; respectively calculating an average pressure value and a pressure variance, further calculating a comprehensive pressure index, and calculating a handwriting feature index and a writer identity authenticity score according to the comprehensive pressure index; calculating a score average value and a score standard deviation of the identity authenticity of the writer, and obtaining a handwriting authenticity index threshold value; calculating an identity authenticity score of the to-be-judged writer, and comparing the score with a handwriting authenticity index threshold to judge the authenticity of the handwriting; according to the method, handwriting recognition and authenticity judgment can be more accurately carried out.

The invention relates to a handwritten character recognition method and device, equipment, a storage medium and a chip, and the method comprises the steps: obtaining handwritten character information which comprises the OCR information of a target subject in an education scene; calling a handwriting recognition model to carry out subject character recognition on the handwritten character information to obtain a corresponding recognition result; the handwriting recognition model comprises m path architectures, m is a positive integer, and the target path architecture is used for performing subject character recognition on the handwritten character information; the target path architecture is a path architecture selected from m path architectures at least based on one of an input mode, a task type, input complexity and input characteristics of the handwritten character information. In this way, the technical problems that in the prior art, a universal model is redundant, the response time is long, and the accuracy of character recognition and the robustness of a model algorithm cannot be guaranteed can be solved.

A method for providing selectable quick actions on handwritten data in a handwritten document is disclosed. The method includes fetching the handwritten data corresponding to strokes made by a user in the handwritten document and recognizing text associated with the received handwritten data by employing a handwriting recognition technique. The method also includes identifying keywords in the recognized text and classifying each identified keyword into an entity type by employing a named entity recognition, a named entity linking, and / or a knowledge graph. In some embodiments, the method includes determining actionable items associated with the classified entity type corresponding to each keyword and creating quick actions, selectable by the user, for each keyword based on the determined actionable items via the knowledge graph. The method includes rendering the created quick actions associated with each keyword on the handwritten document based on a profile associated with the user.

The present disclosure relates to systems, software, and computer-implemented methods for automatically identifying handwritten tips. An example method includes obtaining an image of a receipt, where the image includes a reference region in a pre-set format, a plurality of category identifiers, and at least one set of handwritten characters. The reference region and the plurality of category identifiers can be printed. The plurality of category identifiers can be located at pre-set positions relative to the reference region. The method further includes obtaining optical character recognition (OCR) information of the image and identifying the reference region from the OCR information based on the pre-set format of the reference region. The method further includes determining a plurality of amounts based on the OCR information and the reference region, where at least one of the plurality of amounts is associated with a tip category.

The invention discloses a dictation content generation interaction method based on a large model, and relates to the technical field of educational information processing, and the method comprises the steps: 1, unifying characters, pictures or audios into a reference text, reserving pose anchor points, segmenting the reference text into target units according to semantics, and numbering the target units; 2, combining with the writing duration of a learner portrait prediction unit, and generating a time axis script including a broadcast text, a starting moment, reading duration, pause and a prompt position; step 3, broadcasting and collecting pen leaving, coverage and a password according to a script, determining an actual completion moment, adjusting a next unit to stop according to an error, and recording an execution log; step 4, obtaining an answer image, generating an error cause label through pose matrix correction, handwriting recognition and alignment, and superposing an annotation on a paper surface or a screen; and step 5, writing back the learner portrait and pause according to the execution log and the error cause, generating a next round of time axis script, and finally forming a rhythm closed-loop continuous updating mechanism.

The application relates to the technical field of pattern recognition, and provides a handwriting recognition method and device, electronic equipment and a storage medium, wherein the method comprises the following steps: determining handwriting data to be recognized, the handwriting data comprising matched stroke trajectories and handwriting images; based on a trajectory encoder, trajectory features of the stroke trajectories are extracted, and based on an image encoder, image features of the handwriting images are extracted, the trajectory encoder and the image encoder being obtained through differentiated training strategies; based on a dual-modal decoder, differentiated information between the image features and the trajectory features is applied to fuse the trajectory features and the image features, and handwriting recognition is performed based on the fused features. The handwriting recognition method and device, the electronic equipment and the storage medium provided by the application can significantly improve the recognition capability in a dual-modal data mismatching scene.

The subject technology receives, at a first position in a user interface, a set of input strokes. The subject technology retrieves information corresponding to one or more graphical elements of the user interface, the information comprising a second position of a text input field in the user interface. The subject technology determines, based at least in part on the first position and the second position that at least a portion of the set of input strokes overlaps at least a portion of the text input field. The subject technology performs handwriting recognition on the set of input strokes to provide a set of characters corresponding to the set of input strokes. The subject technology provides the set of characters for insertion into the text input field of the user interface.

We provide the system. [Solution] A means of automatically generating treatment policies and care plans by analyzing patient information using generative artificial intelligence, A method for converting visit records into digital data using handwriting recognition technology and centrally managing the information, A means of automatically generating answers to questions outside of one's area of ​​expertise based on analyzed information, We protect information in secure data centers and implement measures to ensure the strict management of highly confidential information. A system that includes this.

The invention discloses a whole-process data perception and comprehensive evaluation method and system for intelligent homework accompanying, and belongs to the technical field of intelligent education and human-computer interaction. The method comprises the following steps: before homework, generating a dynamic homework plan based on a homework list, student historical data and a real-time state; during operation, academic, behavior and emotion data are collected through a multi-modal sensor and are processed under an end-cloud architecture; in the writing process, handwriting recognition is combined with a knowledge graph to realize real-time discovery and attribution of wrong questions; after operation, a comprehensive evaluation report is generated based on the multi-dimensional evaluation model, feedback is output through a natural language generation module, and the report is stored after being subjected to credible evidence storage processing. The system realizes objective monitoring and personalized evaluation of the whole process of student homework through cooperation of dynamic adaptive planning, real-time error attribution, multi-modal perception fusion and block chain evidence storage, and has the advantages of improving accompanying quality, enhancing report credibility and facilitating long-term tracking.

The invention discloses an end-to-end volley handwritten letter recognition method based on a millimeter wave radar, and belongs to the field of radar data processing. According to the invention, complete radar original data is used to identify volley handwritten letters, and a multi-modal optimization target of a millimeter wave radar end-to-end handwriting identification method based on writing mode guidance is provided. A multi-modal handwriting recognition model for millimeter wave radar original signals and two-dimensional handwritten letter stroke trajectory data is provided, and a targeted neural network training strategy is provided for the optimization target and the model architecture. According to the method, priori knowledge is fused in a training process, so that the extraction complexity of abstract features by a network is reduced, the training convergence of a radar original data feature extractor is accelerated, and the recognition accuracy of the radar original data feature extractor in a complex handwritten letter recognition task is improved.

The invention discloses an optical character recognition method and system for mixing a handwritten form and a printed form, relates to the technical field of computer vision and document analysis, and adopts a handwritten form OCR (Optical Character Recognition) model to carry out handwritten form area detection on an input document image, and the input document image is divided into a printed body internal handwritten form, an independent handwritten paragraph and a table handwritten form. Through a two-channel model architecture and a fine-grained classification strategy, the problems of low detection precision and poor model generalization are solved, and efficient and lightweight mixed text analysis is realized. The improved YOLOv12-L model is based on spatial layout and writing feature classification, can cope with diversified document scenes, and reduces recognition failures caused by scene differences. Through classification optimization, the core problems of false detection, semantic fracture of complex typesetting, table interference and the like in a mixed scene can be solved in a targeted manner, and scene self-adaption of end-to-end handwriting recognition is achieved.

The invention relates to the field of computers, and discloses a method and device for updating a document based on annotation content and a display device. The method comprises the steps of displaying an annotation window above a display window of a target document in response to an annotation operation; wherein the target document can be displayed through the annotation window; in response to a touch operation in the annotation window, displaying a touch track of the touch operation in the annotation window, and determining annotation content according to the touch track; determining an update policy in response to a selection operation in the annotation window; or determining an updating strategy according to the touch track or the annotation content; and updating the target document according to the updating strategy and the annotation content. The transparent window is created above the document, handwriting input is performed on the transparent window, the handwriting input content is automatically recognized by using the handwriting recognition algorithm, and then the original document is updated at the determined updating position according to the recognized content, so that conference records are convenient to organize and display, and the conference efficiency is also improved.

The embodiment of the invention provides a handwriting system and a handwriting recognition method. The handwriting system comprises a writing surface, a handwriting pen and an electronic device, the writing surface comprises at least one optical identification code, and each optical identification code corresponds to one coordinate; the handwriting pen is used for determining a written target content according to a plurality of target optical identification codes passed by a user when the user uses the handwriting pen to write on the writing surface; the electronic device comprises a display screen, and the display screen is used for displaying the target content. According to the scheme, the writing track when the user writes the characters can be identified through the optical identification code on the writing surface, and the writing track can assist in identifying the written characters, so that the accuracy of handwriting identification is improved.

The application is a real-time handwriting recognition method based on deep neural network, aiming to solve the problems of insufficient accuracy, poor real-time performance and poor adaptability to complex writing style in electronic whiteboard handwriting character recognition. The method first collects handwriting trajectory sample data through electronic whiteboard sensors and infrared cameras, and after missing value processing, outlier processing and format conversion, the trajectory coordinates are denoised and normalized. Secondly, enhanced samples are generated by pixel shift, 10-dimensional features such as direction angle, speed and normalized pen tip pressure are extracted, and particle swarm optimization algorithm is used to select the optimal feature subset and splice it into time sequence feature vector. Finally, a 3-layer LSTM network is used to learn the time sequence dependence of the feature vector, and a full connection layer and a softmax function are used to output the probability of 62 classes of characters, and the classification recognition is completed. The method realizes efficient and accurate recognition of electronic whiteboard handwriting characters, improves the real-time performance and reliability of handwriting interaction, and has strong engineering application value.

A method for identifying abnormal handwritten strokes and a handwritten recognition system, comprising the following steps: establishing a first training data set and a second training data set, extracting features from the preprocessed data of the first training data set through a first convolutional network, and extracting features from the preprocessed data of the second training data set through a second convolutional network, and outputting two groups of feature maps; calculating the difference magnitude between these two groups of features in a high-dimensional space and inputting it into a decision model; performing model training, and finding an optimal cluster center data set through a clustering algorithm; the preprocessed test data enters a handwritten recognition model for handwritten recognition to obtain a first recognition result, and the first recognition result passes through the cluster center data set and then enters the decision model and the weight decision formula in sequence to obtain a final second recognition result. The present invention can achieve the purpose of accurately identifying abnormal handwritten strokes, cooperate with the handwritten recognition model, improve the recognition accuracy and speed of handwritten strokes, and has wide applicability.

To suppress inconvenience caused by forced conversion into a predetermined character type.SOLUTION: A display device according to the present invention which performs character recognition on a handwritten input using a dictionary and converts the handwritten input into a predetermined character type, comprises: a handwriting recognition control unit that calculates accuracy information relating to a recognition result for each handwritten character; a handwriting recognition dictionary control unit that uses a first dictionary to recognize the handwritten character and convert it into a first character string of a first character type when the accuracy information exceeds a predetermined threshold, and uses a second dictionary including a second character type different from the first character type predetermined in the first dictionary to recognize the handwritten character and convert it into a second character string of the second character type when the accuracy information is less than or equal to the predetermined threshold; and a display control unit that displays, in a predetermined input field in which forced conversion is performed, the first character string of the first character type recognized using the first dictionary and the second character string of the second character type recognized using the second dictionary.SELECTED DRAWING: Figure 9

The present disclosure includes systems and methods for handwriting recognition. Handwriting data is received. Geometric data of text in handwriting data is determined. Sub-characters of the text are determined. Sub-characters of text are matched to a model. Most probable characters of the text is determined based on the matching.

This invention discloses an electronic pen handwriting recognition method and system based on ultrasonic transmission trajectory. The method includes: receiving handwriting trajectory data transmitted by an electronic pen via ultrasound and received by a sensor array, the trajectory data including spatial coordinate information and corresponding timestamps; performing time alignment and resampling processing on the trajectory data, and obtaining a continuous trajectory sequence by interpolation to compensate for missing trajectory points; constructing a multidimensional dynamic feature sequence containing spatial location features, kinematic features, and trajectory geometric features based on the continuous trajectory sequence; inputting the multidimensional dynamic feature sequence into a two-branch Transformer feature encoding network to extract trajectory spatial structure features and motion change features respectively, and performing feature fusion through a cross-branch attention mechanism; inputting the fused features into a sequence decoder to generate a corresponding character sequence, thereby achieving text recognition of the continuous handwriting trajectory. This invention is applicable to online handwriting recognition scenarios based on ultrasonically positioned electronic pens.

The application relates to the technical field of character recognition, and provides a general-scene character recognition data automatic generation method and system, which comprises the following steps: collecting and establishing a part of character recognition data set A; using a deep learning-based image enhancement method to generate printed data set B; using an adversarial generative network (GAN) to migrate the style of the data set A to the data set B, and expanding to obtain data set C; using a CTC-based character accurate positioning image enhancement method to obtain D and E; and proportionally mixing the data sets A, B, C, D and E for training, wherein the mixed training adopts a feature-based data enhancement method to enrich sample diversity. The application can solve the problem that, in handwriting character recognition, due to the continuous writing of handwriting characters, the handwriting characters are more diverse, meanwhile, diverse fonts and open data sets are lacking, and handwriting recognition is difficult to achieve the same amazing effect as that of typewritten characters, and high-quality and evenly-distributed data are generated through image enhancement, semantic understanding, GAN and other technologies.