643results about "Digital ink recognition" patented technology

The present disclosure generally relates to embodiments for video communication interface for managing content that is shared during a video communication session.

Artificial intelligence (AI) systems of the inventive subject matter are directed to receiving uploaded images containing one or more document, identifying the type of documents received, and returning information contained in those documents. Upon receiving an image containing a document, the AI system: checks for barcodes, OCRs any text, detects visual features, and detects an overall document shape. The AI system can then use any information gathered during those steps to ultimately verify information contained in the document.

A character processing method and apparatus, an electronic device and a storage medium. The method includes: acquiring a first image comprising a to-be-processed character; training a target stroke order determination model by combining with a spatial attention mechanism and a channel attention mechanism; and inputting the first image into the target stroke order determination model trained in advance to obtain a target stroke order corresponding to the to-be-processed character.

The invention belongs to the field of intelligent man-machine interaction, and relates to an intelligent writing and correcting method and system.The method comprises the steps that a flexible pressure sensor matrix and a coordinate positioning system are used for sensing multi-dimensional data generated when an input pen writes, and a four-dimensional writing feature space is constructed; a three-level correction system is adopted, and logical reasoning verification is conducted on the answers through a deep semantic understanding model; the three-stage correction system sequentially comprises OCR font matching, semantic correction based on a large language model and intelligent correction based on a process reward model; a cognitive diagnosis model is introduced, and the answer hesitation degree is judged by analyzing answer indexes; fusing question answering information, utilizing a large model to identify knowledge points to which questions belong, and inferring knowledge mastering weak points according to question answering conditions; writing information is accurately collected through an intelligent writing capture technology based on multi-modal sensing fusion, an intelligent correction integrated method is built in combination with a cognitive diagnosis model, and efficient recognition and accurate correction of written content are achieved.

The invention provides a handwritten mathematical expression recognition model training method, a handwritten mathematical expression recognition method and a handwritten mathematical expression recognition device, a LaTeX sequence is automatically extracted and enhanced from an academic PDF file, a large-scale and high-reality handwritten formula image data set is generated through rendering and material conversion, and then an end-to-end recognition model is trained. According to the scheme, the core bottleneck of training data scarcity and insufficient diversity caused by high manual labeling cost is fundamentally solved; according to the method, the generalization ability and robustness of the model in a real scene are effectively improved by synthesizing a vivid handwritten image; and finally, the identification model realizes a remarkable breakthrough in performance when processing a complex and long sequence formula, and a set of complete and extensible engineering solution is provided for large-scale application of the technology.

The invention discloses a handwritten mathematical formula dynamic recognition method based on multi-modal feature fusion, which comprises the following steps of: 1, performing code conversion on stroke track sequence information contained in data by adopting an HSV color model to generate an HSV pseudo-color image, and fusing the HSV pseudo-color image with a data binary image to obtain a time sequence rich image; 2, feature extraction is performed on the time sequence rich image and the stroke track sequence through a convolutional neural network, image features and stroke features are obtained, the stroke features are enhanced by using the image features, and multi-modal features are obtained after the image features and the enhanced stroke features are fused; step 3, enhancing the input multi-modal features by adopting position coding through a decoder, and performing symbol prediction in combination with an attention mechanism and a multi-scale symbol counting module to generate a mathematical formula symbol sequence; and 4, designing rules, and correcting the generated mathematical formula symbol sequence. According to the invention, the recognition accuracy of confusable characters can be improved.

The embodiment of the invention discloses a mobile handwritten signature recognition method and device based on image processing and a medium, and relates to the technical field of handwritten signature recognition, and the method comprises the steps: obtaining the handwritten signature image information of a handwritten signature input in a mobile terminal, and carrying out the image analysis of the handwritten signature image information, determining a current dynamic threshold value corresponding to each pixel unit; based on the current dynamic threshold value corresponding to each pixel unit, binarization processing is carried out on the handwritten signature image information, a corresponding binarized signature image is determined to extract multi-dimensional signature features of the binarized signature image, and the multi-dimensional signature features comprise image features, dynamic features and geometric features; according to the multi-dimensional signature features and a pre-constructed signature recognition model, carrying out signature recognition on the handwritten signature, and determining recognition information of the handwritten signature; the method of dynamically adjusting the threshold value can significantly reduce errors in the binarization processing process, thereby improving the accuracy of subsequent signature recognition.

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 intelligent whiteboards, in particular to a whiteboard handwritten formula real-time identification method and system based on artificial intelligence. The method comprises the following steps: constructing a handwriting path track based on a touch point sequence, and delimiting a high-density handwriting region; performing symbol detection on the high-density handwriting region to position a potential symbol region, and extracting a stroke feature vector of the potential symbol region through a MobileNetV3-Small model; using the stroke feature vector to fuse the handwriting pressure and the inclination angle feature to construct a symbol relation graph; and performing dynamic planning on the initial LaTeX sequence through a CYK algorithm on the basis of node characteristics and a dynamic adjacency matrix in the symbol relation graph, and converting the symbol relation graph into a LaTeX expression. According to the method, dynamic threshold segmentation is adopted, handwriting pressure and dip angle information are combined, the sampling density and the NMS threshold value are dynamically adjusted, a high-density area detection result is reserved preferentially, the symbol detection accuracy is remarkably improved, and the problem that missing detection or false detection is prone to occurring in a dense writing area in a traditional method is solved.

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 provides an online track point recognition method based on a self-supervised learning model, and belongs to the technical field of deep learning, and the method comprises the steps: obtaining unlabeled sample data in a training set, carrying out the pre-training of the self-supervised learning model, and obtaining a mask encoder; transferring parameters of a mask encoder in the self-supervised learning model to an encoder of an end-to-end classification model, establishing an initial end-to-end classification model by connecting a classification head, training the initial end-to-end classification model through a small amount of labeled sample data in a training set, and obtaining the end-to-end classification model; and an encoder in the end-to-end classification model performs feature extraction on the parameter information of the online track points acquired in a preset time period, and a track point identification result is acquired through classification head prediction. According to the method, on the basis of the self-supervised learning model and the end-to-end classification model, online track points can be recognized, and complex track points are recognized while track point recognition efficiency is 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 present disclosure provides a character recognition model training method and apparatus, a character recognition method and apparatus, a device and a medium, relating to the technical field of artificial intelligence, and specifically to the technical fields of deep learning, image processing and computer vision, which can be applied to scenarios such as character detection and recognition technology. The specific implementing solution is: partitioning an untagged training sample into at least two sub-sample images; dividing the at least two sub-sample images into a first training set and a second training set; where the first training set includes a first sub-sample image with a visible attribute, and the second training set includes a second sub-sample image with an invisible attribute; performing self-supervised training on a to-be-trained encoder by taking the second training set as a tag of the first training set, to obtain a target encoder.

The invention relates to a signature authentication method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring a target signature image of a target user; wherein the target signature image comprises offline signature information of the target user; obtaining a target local feature of the target signature image according to the target signature image based on the trained local network model; inputting the target signature image into the trained global network model to obtain a target global feature of the target signature image and a target weight vector of the target local feature; and authenticating the offline signature information according to the target global feature, the target local feature, the target weight vector of the target local feature and the reference signature image of the target user. By adopting the method, precise authentication of the signature information can be realized.

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.

This application discloses a system for signing medical communication documents based on digital handwriting. The service governance platform Spring Cloud microservice framework is used as the core architecture layer of the business system. The access layer provides an access interface to access the access medium containing the electronic signature collection device; the access layer implements the intermediate authentication of the application layer and each business system of the smart hospital, and provides a service authentication entrance; the application layer provides signing task management, communication document management, signing document verification, and evidence information management services for the medical communication documents generated by each business system; the service layer converts the documents to be signed and unifies the format, calls the corresponding signing terminal to collect the signing handwriting according to the signing content, signing order, and signing position required by the signing task management to generate a signature data packet and merge the telegram to form a signed document, encrypts the signing data and signed documents and stores them; the resource layer supports the service layer, application layer service operation and signing server cluster resource equipment.

Automated document ingestion (ADI) provides a comprehensive system and method to streamline document ingestion automation through developing, deploying, and monitoring machine learning models and tools. The system is designed to integrate alongside existing manual entry pipelines within a company. ADI has multiple components to accomplish each step of this task, namely document enhancements, an augmented data entry user interface, and a machine learning operations (ML Ops) pipeline.

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

The invention discloses an electronic digital signature system and method, particularly relates to the technical field of digital signatures, and comprises a digital signature extraction module, a signature verification module and a batch signature generation module. The method specifically comprises the following steps: S1, digital signature extraction; S2, signature verification; and S3, batch signature generation. According to the method, the review efficiency can be improved, the signature safety is ensured, the signature can be taken at any time, encryption and decryption integrated management is realized, the signature can be generated in batches, the accuracy is ensured, and meanwhile, the signature extraction accuracy is improved by adopting an advanced technology.

The invention discloses an automatic homework correcting system. Comprising a handwriting collection module, an OCR recognition module, a wrongly written character correction module and an intelligent scoring module. The handwriting acquisition module is used for acquiring handwriting; the OCR recognition module is used for recognizing the handwriting; the wrongly written character correction module is used for correcting wrongly written characters; the intelligent scoring module is used for comprehensive consideration; the test paper handwriting is collected and quickly converted into text data, the CR recognition module can accurately recognize the writing content of the test paper, the writing content can be accurately converted into texts even in the face of different writing styles and handwriting characteristics, and the wrongly written character correction module can ensure the text accuracy of examinee answers; the intelligent scoring module is combined with question requirements, a standard question solving process and an examinee answering process, deep analysis is carried out through an LLM large language model, objective and accurate scores and targeted comments can be given, and the accuracy and reliability of examination review are improved.

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

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

The present invention provides a method, system and computing device for recognizing the stroke order of handwritten Chinese characters, the method comprising: obtaining a trajectory point sequence of a handwritten Chinese character to be recognized, and preprocessing the trajectory point sequence of the handwritten Chinese character to obtain a key point sequence of the handwritten Chinese character; extracting the stroke features of the handwritten Chinese character based on the key point sequence of the handwritten Chinese character, wherein the stroke features include basic features and semantic features; and using a trained stroke order recognition model to process the stroke features of the handwritten Chinese character together with the stroke features of the corresponding template Chinese character to obtain a stroke order recognition result. The present invention can obtain handwritten Chinese characters and perform real-time analysis, accurately recognize whether the stroke order of writing the handwritten Chinese character is correct or not, and feed back the recognition result to the user, which can not only greatly improve the recognition efficiency of the stroke order of handwritten Chinese characters, relieve the tutoring and teaching pressure of parents and teachers, but also assist the writer to develop the habit of writing Chinese characters according to the correct stroke order.

A method, system and printing device for rendering digital color changing inks, can involve configuring a digital ink to appear as one color on a digital screen at one angle and as a different color at a different angle, and rendering the digital ink in a digital document. In an embodiment, a black geometric shape can be created, which can turn off RGB pixels. Single-color lines can be then added to the black geometric shape. The geometric shape can be viewed at different angles to determine one or more working patches. One or more working patches can be then added to the digital document. New Digital color changing inks can be created by taking advantage of the RGB output verses angle curves. The digital inks can appear as one color on a digital screen at one angle and as a different color at a different angle.

The present disclosure provides a mid-air-gesture editing method, device, display system and medium, the method is applied to a display device, and includes: recognizing a hand gesture in an acquired first gesture image to obtain a posture category; triggering an editing event corresponding to the posture category, different posture categories corresponding to different editing events; in response to the editing event, processing a mapping position corresponding to at least one frame of a second gesture image, and acquiring a processing result, wherein the mapping position is obtained after mapping a position of a key point in the second gesture image to an editing interface of the display device; and adding the processing result in the editing interface.

The embodiment of the present application provides a brush effect image generation method, an image editing method, a device and a storage medium, the method comprising: responding to an area selection operation on a display interface in a display screen, determining a pattern extraction area in the display interface; obtaining a first brush effect image according to the pattern extraction area, the pattern of the first brush effect image being the pattern corresponding to the pattern extraction area in the display interface; obtaining a second brush effect image according to the first brush effect image, wherein the patterns of the first brush effect image and the second brush effect image are the same, and the clarity of the second brush effect image is greater than that of the first brush effect image. The brush effect image obtained by using the embodiment of the present application has a higher clarity, which improves the user experience.

The invention provides a multi-device cooperation method and device, a medium and electronic device.The method comprises the steps that an identifier and / or a placeholder of a target equation on an answering device are / is recognized, and after the identifier and / or the placeholder of the target equation are / is recognized, the target equation on the answering device is recognized; the answering device obtains a calculation result which is written on a draft device and aims at the target equation; and supplementing the calculation result behind the identifier or replacing the placeholder. According to the invention, the efficiency and accuracy of performing calculation practice by using equipment such as a learning machine can be improved.

The invention discloses a handwritten digital image clustering method, system and device based on tensor anchor point label learning and a medium, and the method comprises the steps: sequentially carrying out the preprocessing, multi-view feature data generation, feature selection and feature extraction, and feature normalization processing of a to-be-clustered handwritten digital image, and obtaining multi-view handwritten digital image feature data; constructing a target function of handwritten digital image clustering based on the multi-view handwritten digital image feature data; introducing an auxiliary variable into the target function, and introducing a penalty term by adopting an augmented Lagrangian multiplier method to obtain a Lagrangian function; solving the Lagrangian function by adopting an alternating iteration method to obtain a clustering result of final division of the handwritten digital image; similarity information from different view angle features is mined through the anchor point technology, the quality of each view angle clustering result is controlled through tensor constraint, the problem that handwritten digital images cannot be efficiently processed through a traditional multi-view angle clustering method is effectively solved, and the method has the advantages of being high in clustering precision and high in efficiency.

The invention provides a drawing projection marriage matching test system based on a visual converter and a method thereof. According to the system, drawing behaviors, emotion feedback and interaction data of a user are collected, multi-modal feature extraction is carried out through a visual converter, and drawing styles, psychological features and emotion preferences of the user are deeply analyzed through a self-supervised clustering algorithm. A unique emotion feedback loop is embedded into a system, and micro expressions, gestures and emotion changes shown by a user in the drawing process are captured in real time, so that more accurate personalized marriage matching recommendation is realized. The system generates a comprehensive love and marriage matching report, provides customized love and marriage suggestions in combination with potential characters, behavior modes and emotional requirements of users, and is matched with emotional management and mental health support tools. The innovative system improves the scientificity, interactivity and deep emotion association of love and marriage matching, has high application value, and can be widely applied to the fields of love and marriage platforms, emotion consultation and intelligent mental health.

The invention provides a handwritten signature recognition method and system fused with a multi-modal technology in the technical field of handwritten signature recognition, and the method comprises the steps: S1, obtaining a large number of handwritten signature images, carrying out the preprocessing and marking of each handwritten signature image, constructing a real data set, and carrying out the sample expansion operation of the real data set; s2, a large number of handwritten documents are obtained, and data synthesis operation is executed based on all the handwritten documents to construct a synthesis data set; s3, creating a handwritten signature recognition model for recognizing the handwritten signature based on the ConvNeXt module and the Vi T module; s4, performing first-stage fine tuning on a handwritten signature recognition model through the synthetic data set, and performing second-stage fine tuning on the handwritten signature recognition model through the real data set; and S5, identifying the handwritten signature through the fine-tuned handwritten signature identification model. The method has the advantages that the accuracy and robustness of handwritten signature recognition are greatly improved.