89 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.

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 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.

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.

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 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.

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

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.

The invention discloses a handwritten character recognition method, a related device, equipment and a storage medium, and the method comprises the steps: carrying out the character recognition based on a handwriting track of a target object, and obtaining a first text; wherein the first text comprises first characters respectively represented by each handwritten character in the handwritten track; performing similarity comparison in a character library of the target object based on the handwritten characters to obtain second characters represented by the handwritten characters; wherein the character library comprises real characters respectively represented by a plurality of reference characters, and the reference characters are handwritten characters historically written by the target object; and replacing the first character represented by the corresponding handwritten character in the first text based on the second character represented by the handwritten character to obtain a second text. According to the scheme, the accuracy of handwriting recognition can be improved.

The invention provides a blackboard-writing display method, electronic equipment and a storage medium, the method is applied to the technical field of interactive tablets, and the method comprises the following steps: displaying a whiteboard application interface; in response to the identification instruction for the whiteboard application interface, identifying the original handwriting on the whiteboard application interface to obtain a handwriting identification result; acquiring voice information recorded in the writing process, and recognizing the voice information to obtain a voice recognition result; generating structured blackboard writing content according to the handwriting recognition result and the voice recognition result; wherein the writing style of the structured blackboard writing content is the same as that of the original handwriting; and displaying the structured blackboard writing content on the whiteboard application interface. According to the method, structuring of the blackboard-writing content is achieved, the readability, the constitutive property and the semantic integrity of the blackboard-writing content are improved, and a user can understand and reuse the blackboard-writing content conveniently.

The embodiment of the invention discloses a conference information display method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring current display information in a multi-person conference scene; obtaining first handwriting information corresponding to the current wearing user, second handwriting information corresponding to the current display information, display video information corresponding to the current display information and conference audio information associated with the current display information; identifying the first handwriting information, and determining a display information processing instruction corresponding to the first handwriting information; based on the display information processing instruction, controlling a device worn by the current wearing user to perform marking processing on the current display information; inputting the second handwriting information, the display video information and the conference audio information into a writing prediction model to obtain a writing prediction result; and displaying the writing prediction result in the current display information. According to the technical scheme, the handwriting recognition and prediction efficiency and accuracy can be improved in a complex multi-person conference scene.

The invention provides a handwriting display method and device, electronic equipment and a storage medium, and the method comprises the steps: displaying a handwriting track corresponding to a handwriting operation in a handwriting region of a handwriting screen under the condition that the handwriting operation is detected; and performing handwriting recognition on the handwriting track to obtain a handwriting recognition result, and displaying the handwriting recognition result in a display area of the handwriting screen. By displaying the different display areas and the handwriting areas on the same screen, a user can synchronously view the handwriting content and the recognition result thereof in the same visual range without frequent switching among the different areas, so that the operation process is simplified, and the conciseness of an interface and the user interaction efficiency are improved; the size of the display area is increased along with the increase of the handwriting recognition result, and the size of the handwriting area is reduced along with the increase of the handwriting recognition result, so that the space of the handwriting area and the display area is reasonably distributed, the natural writing habit of the user is met, and efficient handwriting interaction experience is provided for the user.

The invention belongs to the technical field of image processing, and relates to a handwriting recognition method and device, a medium and electronic equipment. The method comprises the following steps: acquiring a target character image, and performing feature extraction processing on the target character image by using a capsule network to obtain an initial feature map; performing feature weighting processing on the initial feature map to obtain a weighted feature map, and inputting the weighted feature map into a pre-trained first recurrent neural network to obtain a first feature; and inputting the initial feature map into a pre-trained second recurrent neural network to obtain a second feature, and determining an identification result of the target character image according to the first feature and the second feature. The method gives play to the advantage that the capsule network is good at processing the position and attitude change of an object in the image, can understand the hierarchical structure of the image, can remember the information in a long sequence by using the recurrent neural network, is suitable for understanding the time sequence characteristics of characters, has certain advantages in predicting scribble fonts, and has a good application prospect. Therefore, the accuracy and robustness of character recognition are improved.

The application provides a handwriting recognition method and system based on ultrasonic waves, a mobile terminal and a server, the application combines ultrasonic waves with handwriting recognition, proposes a new input method, and through machine learning technology combining CTC loss and LSTM, realizes real-time recognition of handwriting input; the ultrasonic waves after being carried by the autocorrelation sequence improve the robustness to environmental changes, and in a cross-environment experiment, a high letter recognition accuracy is achieved; the machine learning model has good segmentation ability for the interval between words, can support continuous handwriting input recognition, solves the problem that long sequence input is difficult to segment in actual application, and partially solves the cross-environment problem faced by passive sensing, and shows great potential of ultrasonic wave passive recognition in commercial fields.

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 application provides a method, device and system for early warning of Alzheimer's disease risk based on handwriting recognition, which relates to the field of health information processing technology. The method includes: receiving handwriting recognition data from the Bluetooth pen in real time while the assessor is filling out a paper diagnostic scale with a handwriting pen; calculating the test subject's diagnostic scale score based on the handwriting recognition data and determining the test subject's first Alzheimer's disease risk level; using an Alzheimer's disease risk prediction model to determine the test subject's second Alzheimer's disease risk level, and obtaining the test subject's Alzheimer's disease risk warning result data. The present application can be applied to test subjects such as the elderly who are inconvenient to use electronic scales, and can ensure the efficiency and automation of paper scale data processing, can effectively improve the accuracy and reliability of Alzheimer's disease risk warning, and thus can better achieve auxiliary diagnosis of Alzheimer's disease for the test subject.