1943 results about "Handwriting" patented technology

The invention discloses a reading sequence detection method and system combining layout analysis and a language model. The method comprises the following steps: collecting a historical handwritten document image and preprocessing the historical handwritten document image; performing layout analysis on the document image, and identifying and classifying interested text areas in the document image; performing semantic similarity evaluation on different types of layout entities in combination with a language model; and detecting a reading sequence by using a sorting algorithm through document layout and semantic information. The method has the advantages that the reading sequence detection method for the historical handwritten document image is provided through combined recognition of the text and the layout, and conversion of the document from the image to the text content conforming to the reading sequence is achieved.

The invention discloses a writing brush calligraphy practice correction system based on real-time handwriting analysis. The system comprises a sensing layer used for collecting handwriting tracks, physiological signals, environmental parameters and ink mark characteristics; the edge calculation layer is used for executing noise filtering, coordinate system normalization, multi-modal data alignment and feature primary extraction through an Apache Kafka data pipeline; and the algorithm analysis layer is used for carrying out super-long calligraphy stroke sequence modeling by adopting an S4 architecture time sequence model, introducing a Neural ODE module for modeling, capturing dynamic characteristics in a continuous pen wielding process, constructing a multi-physics-field coupled PINN framework, constraining neural network prediction through a physical loss function, deploying a dual-stage characteristic extractor to extract high-order characteristics, and extracting the high-order characteristics. Book style features are extracted, and the current practicing book of the user is classified in real time; the intelligent correction layer is used for generating a dynamic correction suggestion, constructing a personalized learning path, realizing self-adaptive scoring and providing aesthetic dimension feedback at the same time; and the user interaction layer is used for providing an AR correction interface.

The invention discloses an electronic signature handwriting identification method based on a VILT multi-mode model. The method comprises the following steps: 1) collecting electronic signature information of a party; 2) preprocessing the collected information; 3) taking the preprocessed information as a sample; 4) constructing a multi-modal learning model based on a Transform network, namely, a VILT multi-modal model; 5) respectively sending the electronic data information and the electronic image information in the sample into a VILT multi-modal model, and carrying out model training; 6) performing optimization and parameter adjustment on the model by relying on the verification set; 7) taking the electronic signature handwriting of the party needing to be identified as a detected material, and sending the detected material into the trained and optimized VILT multi-modal model to obtain a similarity value; and 9) outputting a detection result. According to the invention, the mapping capability of the electronic image information to the electronic data information is improved, and the accuracy of electronic signature handwriting identification is improved.

The invention belongs to the technical field of image processing, and particularly relates to a handwriting elimination method and system based on fusion layout structure and semantic knowledge. The method comprises the following steps: inputting a to-be-processed document image into a semantic segmentation model, carrying out pixel point segmentation on the to-be-processed document image one by one, and eliminating handwritten forms to obtain a first intermediate document image; utilizing a layout analysis model to determine layout structure information and text information in the first intermediate document image so as to obtain restoration information used for printed form restoration; performing error correction on the text blocks in the restoration information to obtain a second intermediate document image; and performing printed form restoration on the second intermediate document image to output a text image only containing a printed form. According to the method, font missing, font missing and wrong fonts can be effectively repaired, and the influence caused by shooting conditions and a model elimination effect is solved.

The invention discloses an early diagnosis system for Parkinson's disease based on handwriting behavior feature recognition, which belongs to the technical field of Parkinson's disease and comprises a data acquisition module, a data processing module, a model construction module, a test optimization module, a prediction diagnosis module and an intelligent management and control module. The problems that the Parkinson's disease cannot be diagnosed in an early stage based on handwriting behavior feature recognition in the prior art, early intervention and treatment suggestions cannot be provided for patients, and the treatment effect is reduced are solved. Based on the optimal Parkinson's disease early diagnosis model, handwriting behavior real-time data is predicted, Parkinson's disease early diagnosis is carried out, the Parkinson's disease early diagnosis result is determined, a Parkinson's disease early management and control scheme is formulated, Parkinson's disease early patients are intelligently managed and controlled, and the Parkinson's disease early diagnosis and control efficiency is improved. The Parkinson's disease can be diagnosed early based on handwriting behavior feature recognition, early intervention and treatment suggestions can be provided for patients, and the treatment effect can be improved.

The invention discloses a handwritten content display method and electronic equipment, relates to the technical field of terminals, and can reduce the display time delay of handwritten content in the process that a user writes the content on a content interface, improve the display chirality and improve the use experience of the user. In the scheme, the electronic equipment can create a system-level transparent image layer, such as a global image layer, which is independent of the image layer where the content interface is located and overlaid on the image layer where the content interface is located when a user carries out handwriting operations such as scribing, writing, circling and marking on the content interface, and carries out report point prediction based on the handwriting operations of the user and a historical writing track; and drawing a corresponding writing track on the global layer based on the touch report point corresponding to the handwriting operation of the user and the predicted report point.

A handwriting generating method is provided. The handwriting includes at least one handwriting segment formed by a brush, and each handwriting segment is generated by: obtaining brush parameters, wherein the brush parameters are determined according to pen type parameters selected by a user, or are set by the user; acquiring sampling points of the handwriting track, and generating a handwriting track curve according to the sampling points; determining positions of the interpolation points on the handwriting track curve, wherein an arc length between any two adjacent interpolation points on the handwriting track curve is constant; and determining the handwriting width of the position of the interpolation point according to the brush parameters, and generating the handwriting according to the brush parameters and the handwriting width.

The invention provides a child writing disorder detection method and system based on artificial intelligence, and relates to the technical field of data processing, and the method comprises the steps: obtaining original handwriting data of a target child, and synchronously collecting environment parameter data and emotional feature data at corresponding moments; segmenting the original handwriting data according to a time window, and associating the original handwriting data with the environmental parameter data and the emotional characteristic data in the same time period; inputting into a recognition model, automatically analyzing the handwriting behavior of each segment and the corresponding environment and emotional characteristics thereof, and discriminating the abnormality occurring in the handwriting behaviors of the segments; abnormal behavior attribution processing is carried out, abnormities which are judged to be caused by environment or emotion influences are eliminated, and the abnormities which are not influenced by the environment or the emotion are marked as suspected obstacle behaviors; continuous and periodic analysis is carried out, and obstacle behaviors continuously appearing for multiple times are identified; generating a writing obstacle detection report of the target child; according to the invention, autonomy and accuracy of children writing disorder detection are improved.

The invention relates to the technical field of education, and particularly discloses an intelligent accounting teaching method combined with behavior recognition, which comprises the following steps: collecting behavior data of students in an accounting course learning process through a multi-mode sensor deployed in an accounting teaching environment, the behavior data comprises facial expression data, limb movement data, voice interaction data, handwriting writing data and eye gazing trajectory data, a comprehensive feature vector is generated according to a deep learning model, visual features are extracted by using CNN, voice time sequence features are extracted by using RNN, and multi-modal features are fused through an attention mechanism, so that the visual features are extracted, and the visual features are extracted. According to the method, complementary information of different modal data is effectively integrated, and compared with single modal feature extraction, the learning state evaluation accuracy is improved by about 30%, for example, when the understanding degree of a student on a loan accounting method is recognized, the learning state evaluation accuracy is improved by about 30% in combination with handwriting writing fluency and the professional term use frequency in voice interaction, and the learning state evaluation accuracy is improved by about 30%. And the knowledge point mastering degree can be judged more accurately.

The invention discloses a handwritten mathematical formula identification method based on CSP feature fusion, and the method comprises the steps: S1, obtaining image data containing a handwritten mathematical formula, and carrying out the data amplification of the image data to obtain a handwritten mathematical formula image data set; s2, constructing a handwritten mathematical formula recognition network model based on CSP feature fusion; the handwritten mathematical formula recognition network model comprises a feature extraction module, a CSP-based feature fusion module, a multi-scale counting module and an attention decoder module which are connected in sequence; s3, determining a loss function of the handwritten mathematical formula recognition network model, training the handwritten mathematical formula recognition network model according to the obtained handwritten mathematical formula image data set until the loss function converges, and further obtaining a trained handwritten mathematical formula recognition network model, therefore, the handwritten mathematical formula image to be recognized can be recognized. The invention discloses a handwritten mathematical formula identification method. Symbol segmentation is easily influenced by image quality and mathematical formula complexity, accuracy of symbol identification is easily limited by feature extraction and classifier performance, structural analysis is influenced by a hierarchical structure of a mathematical formula, and the number of various symbols is not perceived in the identification process. And the phenomena of missing symbols, mistakenly measuring repeated symbols and the like occur, so that the handwritten mathematical formula is difficult to recognize and poor in precision.

The invention belongs to the cross technical field of artificial intelligence and image recognition, and discloses a cursive script image sequence recognition method for modeling by fusing a stroke structure, and the method comprises the steps: preprocessing and normalizing a cursive script image, eliminating interference information, normalizing the form of the cursive script image, and guaranteeing the stability and effectiveness of a subsequent processing flow; based on stroke decomposition and feature coding of structural analysis, stroke-level structural features are extracted through character skeleton modeling, and cursive writing characters are converted into time series data; modeling a cursive script recognition neural network fused with structural semantics, and recognizing a stroke time sequence by adopting a BiLSTM neural network; an identification output and result optimization module is designed to ensure the final identification quality; through application of scene integration and platform deployment interface design, practical deployment of the model and butt joint of a front-end platform are realized, and a complete cursive script recognition ecology is constructed. According to the method, not only is the structured representation capability of the cursive script image improved, but also the recognition precision and generalization performance of the model on complex handwriting are remarkably enhanced.

The invention relates to the technical field of information digitization, in particular to an archive digitization management method and system and a storage medium. The method comprises the following steps: acquiring original scanning image data of the science and technology handwritten archive; performing regional brightness compensation processing on the original scanning image data to obtain handwriting feature data, the handwriting feature data including handwriting pressure change feature data and ink diffusion feature data; and performing local projection analysis on the original scanning image data according to the handwriting feature data, and performing breakpoint connectivity analysis to obtain standardized character sequence data including character inclination angle data and stroke connection feature data. According to the method, the details of the handwritten document can be better restored through comprehensive analysis of the handwriting pressure and the ink mark diffusion characteristics, the coherence of strokes and the actual form of characters are recognized, and the accuracy of character recognition is remarkably improved.

The invention relates to the technical field of geological data acquisition, and particularly discloses a lossless field real-time acquisition method and system for field data of land quality, and the method comprises the steps: receiving control information inputted by a manager, and creating a dynamic form; determining a sampling point position based on the pattern spot pattern, and synchronously determining an electronic fence of the sampling point position; and sending the sampling point position to the handheld end, obtaining the position of the handheld end in real time, sending the dynamic form to the handheld end when the position is included in the electronic fence, and obtaining land sampling data containing the height based on the dynamic form. According to the method, a management personnel-oriented platform is established, a dynamic form establishment environment is provided, and the form established by the management personnel is received, so that front-line sampling personnel can carry out data filling according to the form and convert an original handwriting mode into a selection mode or a typing mode, and the interaction efficiency is greatly improved.

The invention provides a handwriting eliminating device and method of a touch screen, an electronic handwriting pen and a pen cap of the electronic handwriting pen, the handwriting eliminating device comprises a trigger component and a strain component, the handwriting eliminating device can be applied to the electronic handwriting pen, and when the handwriting eliminating device is used for eliminating an image on the touch screen, the handwriting can be eliminated. The pressing part transmits force borne by the trigger part in contact with the touch screen to the strain induction assembly, and the strain induction assembly deforms according to the pressing force to generate an erasing signal corresponding to deformation. When the contact force between the trigger part and the touch screen is changed, the deformation degree of the strain sensing assembly is correspondingly changed, and a corresponding erasing signal is generated; the touch force of the trigger part and the touch screen is different, the strain sensing assembly can generate different erasing signals, a user can erase images on the touch screen in a grading mode by adjusting the contact force of the trigger part and the touch screen, and then erasing effects of different degrees are achieved.

The invention discloses a handwriting rendering method, a related device, an electronic device and an intelligent blackboard. The method comprises the steps of at least determining position coordinates of a touch center point based on current touch data collected by the infrared touch screen at the current moment; generating attribute information of a current track point corresponding to the current touch data based on the attribute information of the historical track point and the position coordinate of the touch center point; wherein the attribute information at least comprises position coordinates, speed and acceleration, and the historical touch data corresponding to the historical track point and the current touch data belong to the same section of touch track; and on-screen rendering data of the current track point is generated based on the attribute information of the current track point. According to the scheme, the generation quality of the on-screen rendering data generated based on the infrared touch screen acquisition data can be improved.

The invention relates to the technical field of information security, and discloses a handwriting dynamic encryption and electronic signature method based on a national secret algorithm, and the method comprises the steps: obtaining the handwriting data of a user, and carrying out the preprocessing of the data to remove noise and errors; carrying out feature extraction on the preprocessed handwriting data, extracting corresponding track features, speed features and pressure features, and generating a feature hash value of a unique identifier through a cryptographic SM3 hash algorithm; splicing and combining the feature hash value and a user private key, deriving a dynamic key through a national secret SM2 algorithm, and generating an electronic signature by using the dynamic key; an electronic signature is combined with a timestamp to form a structured signature packet, and the signature packet is hierarchically encrypted and stored in a database through a national cryptographic SM4 algorithm. According to the method and the system, the handwritten signature picture, the digital signature technology, the electronic signature technology and the biological recognition and layout file processing technology are fused, so that the safety and the reliability of electronic document signing are improved.

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 relates to the technical field of natural language processing, in particular to a mental health intelligent assessment method and system based on text feature fusion. The method comprises the following steps: acquiring a handwriting image of a subject, carrying out handwritten character recognition based on an OCR (Optical Character Recognition) technology to pre-process the image and text data, and collecting a psychological questionnaire after the handwriting image of the subject; according to the preprocessed image and text data, multi-scale handwriting features are extracted based on an improved MobileVit model, personalized handwriting features are extracted based on standard word stock comparison, text semantic features are extracted based on an improved Bert model, and then cross-modal feature fusion is carried out; cross-modal fusion features are analyzed and predicted through a large-scale pre-training model, and intelligent assessment of mental health is achieved; and in combination with the psychological questionnaire condition, the psychological health intelligent assessment result is verified and evaluated, so that the accuracy and reliability of the assessment result are ensured. According to the invention, intelligent and accurate evaluation of the mental health state of the subject can be realized.

The present invention discloses a glass handwriting board, comprising a shooting fill light bar assembly, a handwriting glass assembly, a control box assembly and a shooting rod assembly, wherein the shooting fill light bar assembly is arranged on the front edge of the handwriting glass assembly, the control box assembly is arranged at the middle position of the bottom of the handwriting glass assembly, and the rear side of the control box assembly is connected to the shooting rod assembly. The present invention integrates the shooting rod assembly with the handwriting glass assembly through the control box assembly, so that the space occupied by the device is small; at the same time, by arranging the shooting rod assembly behind the handwriting glass assembly, the shooting rod assembly can shoot the writing content on the handwriting glass assembly, and the teacher in front of the handwriting glass assembly can also be photographed at the same time, which can strengthen the interaction between teachers and students, thereby effectively improving the teaching quality.

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.

The invention discloses an interactive teaching method and system based on intelligent paper and pens, belongs to the technical field of educational informatization, and aims to solve the technical problem of how to keep traditional teaching and paper and pen writing habits, realize no-disturbing and whole-process interactive teaching, enhance teaching interactivity and improve teaching efficiency at the same time. According to the technical scheme, the method comprises the following steps: configuring student terminal equipment: distributing an intelligent pen and a specially-made code book for each student; configuring a teacher teaching end, wherein a teacher initiates a paper-pen interaction link by using a large-screen teaching tool; the intelligent pen captures writing actions in real time through a built-in sensor, and handwriting is converted into digital signals through a code book; data acquisition and transmission; processing and storing data; large-screen teacher teaching end data display; reading and feeding back at the teacher end; teaching interaction and evaluation; and optimizing and upgrading.

A handwriting geometry recognition and calibration system by using neural network and mathematical feature includes: a pre-processor for pre-processing coordinate points of geometric figures from user's handwriting so as to get a plurality of sample points which expresses the geometric figures to be recognized; a neural network connected to the pre-processor for receiving the sample points of the geometric figure and recognizing the geometric figure so as to acquire a coarse class of the geometric figure; and an mathematical logic unit connected to the neural network for receiving recognition results from the neural network, including coarse classifications which are used in a secondary classification by using conventional mathematical recognition logics so as to determine an exact geometry shape of the geometric figure; then the geometric figure being calibrated so as to get a geometry with a regular shape.

The invention provides display equipment and a writing optimization method, which are used for adjusting the length of predicted handwriting based on a screen refreshing opportunity and effectively reducing handwriting delay. The display device includes a display unit and a control circuit, wherein: the display unit is configured to display content; the control circuit comprises a processor and a memory, the processor is used for reading a program in the memory and executing the following steps: receiving current handwriting information handwritten by a user, and the current handwriting information comprises a handwriting point position; the next refresh moment of the display unit is obtained, a prediction position corresponding to current handwriting information at the next refresh moment is determined, and the current handwriting information comprises handwriting information received before the next refresh moment; and determining predicted handwriting of the current handwriting information according to the predicted position corresponding to the current handwriting information, and drawing and displaying the predicted handwriting of the current handwriting information.

The invention belongs to the field of information technology and man-machine interaction, and relates to an electronic handwriting pen device based on an inertial sensor and a handwriting recovery method and system. The electronic handwriting pen device comprises a handwriting pen and an IMU (Inertial Measurement Unit) sensor, wherein the IMU sensor is fixed on a pen body of the handwriting pen. The handwriting recovery method comprises the steps that an electronic handwriting pen device based on an inertial sensor is adopted to collect data frames of an IMU sensor in real time, and a data sequence is generated; extracting time domain features and frequency domain features according to the generated data sequence, detecting a pen event according to the extracted time domain features and frequency domain features, identifying a writing state and dividing data intervals; calculating the stroke track of the pen falling interval and the stroke displacement of the pen lifting interval; and performing handwriting recovery according to the stroke track of the pen falling interval and the stroke displacement of the pen lifting interval. Original writing experience is reserved to a large extent, the recovery process is automatically carried out, a user does not need to intervene in operation, the smooth recovery effect can be achieved, and the method has good actual popularization value.

The invention belongs to the technical field of vacuum sealer products, and particularly discloses a vacuum sealer with characters, which comprises a base and an upper cover, the upper cover is movably hinged with one side of the base, a vacuum pump, a control circuit board and a power line are arranged on the base, and upper and lower vacuum cavities are arranged at the corresponding positions of the binding surfaces when the base and the upper cover are closed. Sealing rubber rings are arranged on the periphery of the upper vacuum cavity and the periphery of the lower vacuum cavity respectively, a sealing heating piece assembly is arranged on the front side of the base, a heat sealing strip is arranged at the corresponding position of the front side of the upper cover, a plurality of character belts and character belt driving devices are arranged in the upper cover / base, and an elastic character hot stamping device is arranged at the corresponding position in the base / upper cover. The vacuum sealing machine is simple in structure, convenient to use, light and miniaturized in appearance, capable of achieving hot stamping of various characters, clear in handwriting, high in heating efficiency, capable of synchronously completing sealing and hot stamping of the characters, capable of being disassembled without additional tools, convenient to clean and maintain, long in service life, good in stability and low in manufacturing cost.

In some examples, an electronic device including a touch screen can sense a stylus proximate to the touch screen. For example, the user can draw or write with the stylus to create a simulated drawing or simulated handwriting displayed on the touch screen. In some examples, the electronic device can sense a series of “points” corresponding to the location of the stylus over time while the drawing / writing input was provided. In some situations, noise in the stylus data can cause the electronic device not to sense one or more noisy points included in the drawing or handwriting input. In some examples, the electronic device can interpolate the noisy points based on sensed points before and / or after the time of the skipped points.

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 an international Chinese education handwritten composition picture-oriented intelligent correcting and scoring platform, which is characterized in that the platform framework comprises a front-end display layer, a gateway control layer, a business service layer, a data processing layer and a basic storage layer, wherein the business service layer comprises a login module, a student management module, a teacher management module, a homework management module, a class management module and a correction module, and the correction module comprises image preprocessing, OCR recognition and proofreading, error correction sensitivity adjustment, intelligent correction marking, manual correction and result exporting. Compared with the prior art, the method has the advantages that the handwritten composition picture of a foreign learner is corrected by utilizing CLIP image recognition and a Qwen large language model, a fine-grained marking standard system and a correcting marking tool are developed, one-key intelligent correcting marking is realized, meanwhile, a correcting result can be reviewed and corrected for the second time, and the correctness of the handwritten composition picture of the foreign learner is improved. The correction accuracy is improved, high-quality labeling data can be accumulated to serve as training samples, and the performance of the intelligent correction model is continuously optimized.

The invention relates to a handwriting identification method fusing Mamba2 and mixed experts, an intelligent terminal and a storage medium. The method comprises the steps of obtaining to-be-identified electronic handwriting verification data; inputting the to-be-identified electronic handwriting verification data and the corresponding electronic handwriting reserved sample data into a pre-trained fusion model; the fusion model is obtained by collecting multiple pieces of electronic handwriting data of multiple writers to construct a positive sample pair and a negative sample pair of a training set, performing feature preprocessing on each piece of electronic handwriting data and then performing training; the fusion model receives the electronic handwriting verification data and the electronic handwriting reserved sample data as input and outputs an identification result of the electronic handwriting verification data, and the identification result is used for indicating whether the electronic handwriting verification data to be identified and the electronic handwriting reserved sample data to be identified are from the same writer or not. The global features of the electronic handwriting data can be effectively captured, and the electronic handwriting identification capability with efficient calculation and memory management characteristics is achieved.

The invention provides a handwritten character beautifying method and electronic equipment, on one hand, information of a first handwritten character can be obtained, and on the other hand, a fusion style can be determined through information of a first gear, a handwritten character library and a target font. The fusion style can be used for representing a style after the target font and the handwritten font corresponding to the character in the handwritten font library are fused, and finally generating a first beautified character corresponding to the first handwritten character based on the information of the first handwritten character and the fusion style. According to the method and the device, the handwritten characters of the user can be beautified based on the first gear selected by the user, and the styles of the handwritten characters of the user can be reserved in the finally presented handwritten character beautification effect.