1307 results about "Handwriting" patented technology

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 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 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 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.

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.

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 handwriting real-time prediction method based on a hybrid time sequence convolutional network. The method comprises the following steps: acquiring real track data of a user in a touch screen writing process; preprocessing the real trajectory data to form a multi-dimensional time sequence feature sequence with a preset length; inputting the multi-dimensional time sequence feature sequence into a trained trajectory point prediction model constructed based on a hybrid time sequence convolutional network, and outputting predicted trajectory coordinates; on the basis of the real trajectory data and the predicted trajectory coordinates, trajectory smoothing processing is carried out, and predicted trajectory data used for drawing is generated; and drawing the predicted trajectory data on the touch screen in real time. According to the method, hardware delay is compensated from the algorithm level, writing chirality and real-time performance are improved, dependence on high-end hardware is reduced through end-to-end optimization, and low-cost and high-simulation-degree paper and pen writing experience is provided for touch equipment.

A font generation method and apparatus, and a method and apparatus for training a font generation model. The font generation method comprises: acquiring a target text content vector of a target handwritten font image on the basis of a font generation model; inputting the target handwritten font image into a style branch of the font generation model, so as to extract a target text style vector, and determining a handwriting weighted style vector on the basis of the target text style vector, wherein the handwriting weighted style vector is obtained by means of performing weighted summation on basic font style vectors of a plurality of basic font styles; and acquiring a synthesized style font image, which is generated by a decoder of the font generation model on the basis of the target text content vector and the handwriting weighted style vector. The method can meet diversified requirements of a user for font styles and significantly improve the flexibility of font beautification effects.

A method of identifying devolved sequences of handwriting motions is described. The method includes obtaining, via sensors of a wearable device of a computing system, data corresponding to a user attempting to perform a sequence of handwriting motions associated with one or more target inputs while wearing the wearable device. The method includes identifying, based on at least (i) the data corresponding to the user attempting to perform the sequence of handwriting motion and (ii) the one or more target inputs associated with the sequence of handwriting motions, a devolved sequence of handwriting motions to suggest to the user for inputting a respective target input of the one or more target inputs. The devolved sequence of handwriting motions is a different sequence and includes fewer handwriting motions as compared to the sequence of handwriting motions. And the method includes presenting a representation of the devolved sequence of handwriting motions.

According to the dot-matrix pen handwriting screen projection method and system, handwriting data obtained from a dot-matrix pen is sent to a protocol conversion module through a Bluetooth end data bridge, the protocol conversion module converts the handwriting data into SDK layer protocol data and sends the SDK layer protocol data to a data center service, stable and rapid transmission of dot-matrix pen trajectory data is ensured, and the accuracy of dot-matrix pen handwriting screen projection is improved. The SDK layer protocol data is analyzed through the data center service to obtain target data, the target data is sent to the conference service to bind the conference service through the soft terminal, accurate binding is achieved, the accuracy and management efficiency of writing records in a multi-user use scene are improved, and based on a soft terminal binding result, the writing record management efficiency is improved. And sending the target data to a track service for storage, and sending the target data to a smart television for rendering display, so that a perfect dot matrix pen management system can carry out flexible resource allocation and authority management according to different use scenes, and the efficiency of the dot matrix pen is fully exerted.

The invention relates to the technical field of handwriting analysis, and discloses a handwriting analysis method and system based on emotion calculation multi-modal recognition, and the method comprises the steps: obtaining handwriting data of a writer, and obtaining handwriting features through employing a multi-modal collection technology, specifically, dividing a writing process into a plurality of writing time periods according to time, and each time period does not exceed 5 minutes; collecting pen point pressure data by using a pressure sensor, collecting writing speed data by using a speed sensor, and collecting font structure data by using an image sensor; for each section of handwriting, executing a feature extraction strategy, fusing dynamic and static features, and generating a multi-modal feature set; inputting the feature set into a pre-trained emotion calculation model; multi-modal fusion breaks through the limitation of a traditional single mode, handwriting features are comprehensively described through a 200 + index, dynamic and static dimensions are combined, and the comprehensiveness of analysis is improved; the 300 + submodel and the million-level sample library ensure the analysis precision, the deep learning architecture captures feature association, and the generalization ability of the model is enhanced through cross validation and online learning.

The invention provides a plug-and-play supported localized digital signature terminal, which comprises a file preview module for acquiring an incoming file, previewing the file, adapting to a layout file, positioning a signature position, calling a handwriting signature acquisition terminal device to acquire an electronic signature handwriting time sequence feature sequence or fingerprint stamping image data, and sending the electronic signature handwriting time sequence feature sequence or fingerprint stamping image data to the file preview module; electronic signature strokes are obtained, and a synthesis module synthesizes the signature strokes or / and the stamped images into signatures or / and signatures locally, echoes and adds the signatures or / and signatures to the signing position of the layout file; the cryptographic module adopts a national cryptographic algorithm to calculate digital signatures and encrypted data packets for electronic files, signature handwriting data and stamped data, and the auxiliary module stores, certificates and counts signature contents and signature operation logs in a local terminal; and the identity assistance and authentication module binds the electronic signature handwriting data with the identity information of the signer or / and verifies the identity of the signer according to the handwriting data.

The invention provides an electronic whiteboard writing track optimization method and device, equipment and a storage medium, and the method comprises the steps: obtaining the coordinates, collection time, pressure values and inclination angles of a plurality of sampling points for writing on a screen of an electronic whiteboard through an intelligent pen, determining a central sampling point, and obtaining a writing track of the electronic whiteboard according to the coordinates and the collection time; the method comprises the following steps: determining speed information and acceleration information of a central sampling point according to coordinates of a Bessel curve, dynamically determining the order of the Bessel curve to determine a control point set, determining the instantaneous speed of each control point in the control point set according to the coordinates, and calculating the instantaneous speed of each control point according to the instantaneous speed, the pressure value, the inclination angle and the corresponding specified weight of each control point. According to the method, the width attribute value of each control point is determined, so that the writing track is smoother and has good impression, the width attribute value of each control point is determined based on various factors such as the instantaneous speed, the pressure value and the inclination angle, the line width of the target writing track is adjusted, the real handwriting feeling can be restored, and the writing experience is improved.

The invention discloses a method for achieving electronic file delivery and remote signing based on handwriting envelope decryption and stabbing. The method comprises the steps that a sending end determines documents needing to be delivered and to be signed, configures signing information of a recipient, generates a virtual envelope, encrypts the envelope with an original handwriting electronic signature of the recipient as a secret key and sends the encrypted envelope; the receiving end analyzes the handwriting envelope so as to compare the handwriting characteristics of the electronic signature signed by the sender on site with the original handwriting electronic signature, and decrypts the handwriting envelope; and when the recipient signs, the signed handwriting features are extracted for comparative analysis, and if the comparison succeeds, a signing event is recorded, and a signed document is generated and pushed to the delivery end. The electronic document remote delivery and signing system which is safe, efficient and easy to operate is constructed based on the unique handwriting characteristics of the user, and the problems of data safety, data leakage and electronic document signing handwriting verification can be solved.

The invention relates to the technical field of computer data processing and mode recognition, and discloses an intelligent handwriting record analysis system supporting translation process traceability and capability assessment, which comprises the following steps: collecting multi-dimensional handwriting data generated when a user translates by using an intelligent pen; the Hidden Markov model decodes the handwriting into translation behavior primitive sequences such as smooth writing, hesitant pause and the like; automatically dividing semantic regions such as a main translation region and a draft region by a density clustering algorithm; performing space-time fusion attribution to generate a space-time attribution log for accurately recording behaviors, time and positions; and establishing a multi-dimensional capability index system containing translation efficiency, quality and strategies based on the log, and carrying out quantitative calculation and generating a visual user capability portrait. According to the method, the invisible translation cognition process is converted into traceable and quantifiable objective data, deep insight and comprehensive evaluation of the translation process are achieved, and a scientific and efficient analysis tool is provided for translation teaching and interpreter ability evaluation.

The invention discloses an intelligent paper marking system based on image cutting and recognition, belongs to the technical field of intelligent paper marking, aims at solving the problem that the answer quality of students cannot be comprehensively and accurately evaluated, and comprises an image acquisition module which is used for acquiring test paper images, automatically adjusting scanning parameters according to the size and color mode of test paper, supporting multi-angle scanning and acquiring the test paper images; the image preprocessing module comprises an image enhancement unit, a tilt correction unit and a binarization processing unit and is used for carrying out brightness and contrast adjustment, deblurring, tilt correction and binarization processing on the acquired image; through the image cutting, character recognition and semantic understanding technologies, the answer content, including complex handwritten fonts, altered characters and the like, on test papers in various formats can be accurately recognized, misjudgment caused by non-standard formats or writing problems is avoided, the scoring result is more objective and accurate, and the real answer level of students can be better reflected.

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 provides a power plant work ticket static inspection method and system. The method comprises the following steps: scanning a work ticket to be inspected to obtain a work ticket image, identifying the work ticket image, establishing a work ticket list, and screening a work ticket template corresponding to the work ticket list; checking whether the to-be-signed position is signed according to the work ticket template, and if yes, determining the authenticity of the signed handwriting; if the handwriting is true, checking whether the time to be filled is filled according to the work ticket template, and if so, determining whether the steps executed according to the work ticket are continuous in the forward direction; if the positive direction is continuous, determining whether the safety measures in the work ticket list are correct and complete, and whether the safety measures are executed; if the safety measures are correct and complete and are executed, whether the stamping position and content are correct or not is recognized; and if the stamping position and content are correct, the to-be-inspected work ticket is determined to be qualified, and the inspection accuracy and the inspection efficiency of the work ticket are effectively improved by automatically scanning, identifying and verifying the to-be-inspected work ticket.

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 discloses a handwritten text recognition method and device, equipment and a storage medium, and belongs to the field of text recognition. The method comprises the following steps: acquiring a handwritten text image; extracting handwriting style characteristics according to the handwritten text image, wherein the handwriting style characteristics are used for reflecting a writing style corresponding to the handwritten text image; calibrating a feature extraction position of the handwritten text image according to the handwritten style feature, and extracting a text feature of the handwritten text image according to the calibrated feature extraction position, the text feature being used for reflecting a text in the handwritten text image; and predicting a prediction text corresponding to the handwritten text image according to the text features. According to the method, the accuracy of extracting the text features of the handwritten text image can be improved, so that the recognition performance of handwritten texts of different writing styles is improved.

The invention discloses a character recognition method and device for a handwritten image, terminal equipment and a storage medium, and the method comprises the steps: obtaining the handwritten image, and converting the handwritten image into a single-channel gray-scale map; wherein the single-channel grey-scale map is an image with a white background and black characters; performing reverse binarization processing on the single-channel grey-scale map to generate a reverse binarization image with black-matrix white characters; all contours in the reverse binarization image are recognized, and the contour with the largest area serves as a character core area; calculating an amplification coefficient according to the width and height of the reverse binarization image; performing boundary expansion on the character core region according to the amplification coefficient, and performing cutting based on the expanded boundary to obtain a cut image; and performing character recognition according to the clipped image. According to the invention, the character recognition accuracy can be improved.

The utility model relates to the technical field of correction, and discloses a student sitting posture corrector which comprises a learning board, a sleeve column is arranged on the learning board, a supporting column is arranged on the sleeve column, a chest support is fixedly connected to the top of the supporting column, and a liquid crystal handwriting board is arranged on the learning board. The supporting column is inserted into the sleeve column, strip grooves are symmetrically formed in the two side walls of the supporting column, a connecting rod penetrates through the sleeve column and penetrates through the two strip grooves, a button is fixedly connected to one end of the connecting rod, a spring groove is formed in the outer side of the sleeve column, the button is located in the spring groove, the inner side face of the button abuts against a spring, and the other end of the connecting rod is in threaded connection with a limiting disc. The liquid crystal handwriting board is environment-friendly and economical, the liquid crystal handwriting board can be repeatedly used, contents can be clearly seen through one key, paper is not needed, waste of the paper is greatly reduced, and the liquid crystal handwriting board is simple in structure and convenient to disassemble and assemble.