1070 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 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 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.

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 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.

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 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 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 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.

The liquid crystal handwriting board comprises an electric control module, a liquid crystal handwriting film, an electromagnetic antenna board and a handwriting substrate, the liquid crystal handwriting film, the electromagnetic antenna board and the handwriting substrate are sequentially arranged from top to bottom, and the electric control module comprises a power supply unit, a main control unit, an electromagnetic control unit, a liquid crystal driving unit and a data transmission unit. According to the liquid crystal handwriting board, on one hand, the electromagnetic control unit transmits acquired writing / erasing coordinate information to the intelligent display equipment through the data transmission unit, so that the intelligent display equipment synchronously displays writing / erasing handwriting; on the other hand, the electromagnetic control unit transmits the collected erasing coordinate information to the main control unit through the data transmission unit, so that the main control unit completes handwriting erasing by controlling the liquid crystal driving unit.

The invention provides a writing feedback method, system and device for an electronic whiteboard and a storage medium, and the method comprises the steps: collecting original handwriting coordinate data generated by a touch screen when a user writes on the touch screen of the electronic whiteboard, and obtaining corresponding stroke parameters; dynamically selecting a currently applicable target sampling processing strategy based on the stroke parameter; based on a target sampling processing strategy, sampling the original handwriting coordinate data to obtain target handwriting coordinate data; calling the target kernel event queue to transmit the target handwriting coordinate data to a target event processing thread; and calling the target event processing thread to render the target handwriting coordinate data so as to display the handwriting written by the user on the touch screen. According to the application, real-time writing response of the electronic whiteboard can be realized, writing feedback delay of the electronic whiteboard can be reduced, and writing experience of a user can be improved.

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

The invention discloses a volume and noodle retrieval matching method and device and a storage medium. The method comprises the steps that a volume and noodle library is constructed; acquiring a first volume image; identifying and erasing handwritten handwriting; and paper surface information matching. According to the method, the volume information of the template is retrieved in a text matching mode, and compared with simple image similarity matching retrieval, the speed is higher, and the accuracy is higher. Moreover, according to the method, the front surface and the back surface of the test paper are respectively retrieved, and the confidence coefficient of the retrieval result is calculated according to the text word numbers of the front surface and the back surface of the retrieval result, so that the accuracy and the credibility of the retrieval result can be improved, and the method can be suitable for retrieval and matching of the test paper surface information of more subjects.

The invention discloses a handwritten medical record OCR recognition system and method based on deep learning. The system comprises an image acquisition and preprocessing module, a text detection and region positioning module, a handwritten character recognition module, a semantic correction module and a result storage and export module. The image acquisition and preprocessing module is used for receiving the original medical record image and performing image quality improvement processing to obtain optimized image data; the text detection and region positioning module is used for detecting a text region from the optimized image data and positioning the position information of the text region to obtain text region data; the handwritten character recognition module is used for performing recognition processing on a character sequence in the text region data to obtain an initial text recognition result; the semantic correction module is used for processing the initial text recognition result based on medical priori knowledge to obtain a corrected text recognition result; and the result storage and export module is used for outputting the corrected text recognition result to a target system in a structured format.

The utility model 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 an upper vacuum cavity and a lower vacuum cavity are arranged at corresponding positions of fitting 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.

The embodiment of the invention discloses a handwriting image identification method and system based on artificial intelligence, and the method comprises the steps: firstly collecting a to-be-identified handwriting image and a known sample handwriting image, and generating a target handwriting image set; sequentially performing stroke region segmentation, stroke feature extraction and texture mode analysis on the handwriting images in the set to generate a mixed feature data set containing a traditional handwriting feature set and a deep learning handwriting feature set; inputting the mixed feature data set into a Triplet network model, and generating a first discriminative feature vector of the to-be-identified handwriting image and a second discriminative feature vector of a known sample handwriting image through triple loss function joint optimization training; and finally, generating a handwriting image identification report containing the feature matching score and the author affiliation probability based on the first discriminative feature vector and the second discriminative feature vector. The accuracy and reliability of handwriting identification are improved.

The utility model provides a blood collection tube multi-label extension film which comprises a transparent base film with an adhesive layer on the bottom surface, a red emergency treatment mark is arranged at the top of the surface of the transparent base film, a non-transparent protective film covers the surface of the red emergency treatment mark, and the left side or the right side of the transparent base film is connected with an extension part. The left side or the right side of the transparent base film is detachably connected with a transparent expansion wing through an extending part, N + 1 rows of standard label positions are preset on the back face or the surface of the transparent expansion wing, N rows of standard label positions and a row of handwriting areas are correspondingly preset on the surface or the back face of the transparent expansion wing, and the handwriting areas are located below the last row of standard label positions. A matte coating is arranged on the surface of the handwriting area, N is a positive integer larger than or equal to 2, and a weak adhesive layer with the adhesive strength smaller than that of the adhesive layer on the bottom face of the transparent base film is arranged on the standard label position face. Elastic adjustment of the label capacity is achieved through the transparent expansion wings, handwriting input is met, and the red emergency treatment identification is arranged at the top of the surface of the transparent base film and is easy to recognize and not prone to falling off.

The invention discloses a handwritten content removal method and device, equipment, a storage medium and a program product, and relates to the technical field of image processing, and the method comprises the steps: carrying out the multi-scale feature extraction of a first picture containing handwritten content, and obtaining the features of multiple scales; performing non-local feature extraction on the features of any scale to obtain global features; performing multi-level decoding based on the global feature, the multi-scale features and the first picture to obtain a second picture; the second picture is a result obtained by erasing the handwritten content of the first picture. The handwritten content in the picture with the color background can be quickly and accurately removed on the basis of the method, and a scheme for effectively removing the handwritten content in a picture scene with the color background is provided.

The invention discloses a personalized art teaching capability evaluation and course generation method and system, and the system comprises a handwriting dynamics analysis module which is used for collecting handwriting dynamics data in the creation process of a learner, and generating handwriting feature data; the learner portrait construction module is used for receiving the handwriting feature data and constructing a five-dimensional learner portrait including art ability, learning ability, psychological features, professional interests and personal aesthetic appreciation in combination with the basic information of the learner; the knowledge graph module is used for constructing a knowledge graph in the art professional field; the ability evaluation module is used for generating an ability evaluation result of the learner based on the five-dimensional learner portrait and the knowledge graph by adopting a three-layer neural network structure; and the course generation module is used for classifying the learners into a single-subject type, a single-ability type, a single-project type or a comprehensive type according to ability evaluation results of the learners, and generating corresponding personalized learning paths and course contents based on classification types.

A handwriting system including a teaching device and a practicing device is provided. The teaching device is communicatively connected to a network and displays a setup interface which includes a setup window for obtaining practice settings. These practice settings are then transmitted to the network. The practicing device is communicatively connected to the network and displays a user interface for obtaining handwriting information which is transmitted to the network. The practicing device receives an inspection result from the network and displays a feedback message according to the inspection result. A handwriting method and a non-transitory processing device-readable medium are also disclosed.