352results about "Digital ink recognition" patented technology

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 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 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 relates to a method for realizing electronic screen writing personalization based on an AI writing model, and relates to the technical field of writing personalization, and the method comprises the following steps: obtaining writing data of a target user; standardizing the written data to obtain standard written data, and constructing a writing style model based on the standard written data; performing personalized analysis on the writing style of the target user by using the writing style model, and generating a personalized writing style; the personalized writing style is rendered to the target electronic screen through an electronic screen rendering module, and the personalized writing effect of the target user is displayed in real time. According to the method, the writing details of the user are analyzed through the deep learning model, the personalized writing style is generated, accurate reduction of the writing style is achieved, the personalized writing effect is rendered in real time in combination with the real-time input data and the style model, it is guaranteed that the fluency is consistent with the dynamic characteristics, and the writing experience and autonomy are improved.

This disclosure relates to a hand-drawn graphic recognition method, apparatus, and system, and a computer-readable storage medium. The hand-drawn graphic recognition method includes: receiving an input of a hand-drawn graphic, the input including a stroke set, the stroke set including at least one stroke, each stroke including an information sequence of a plurality of trajectory points, information of each trajectory point including coordinates and writing time of the trajectory point; determining whether the stroke set forms a closed graphic according to coordinate data of the input stroke; and in the case where the stroke set forms a closed graphic, recognizing a category of the closed graphic based on the coordinate data of the stroke.

This disclosure describes systems, methods, and devices for analyzing and providing suggestions for handwritten math characters entered on a device A method may include receiving first handwritten strokes digitally entered on the device by a user; inputting the first handwritten strokes into a machine learning model; classifying, by the machine learning model, the first handwritten strokes as first mathematical characters; causing the device to present, based on the classifying, a selectable indication of the first handwritten strokes; receiving a first user selection of the selectable indication; causing the device to present, based on the first user selection, one or more selectable suggestions for second mathematical characters to follow the first mathematical characters; receiving a second user selection of a first suggestion of the one or more selectable suggestions; causing the device to present, based on the second user selection, synthesized second handwritten strokes representing the second mathematical characters.

Technology is described herein for facilitating a user's interaction with a digital ink document. The technology internally represents the ink document using a data structure having a hierarchy of nodes. The nodes describe respective elements in the ink document. The technology leverages the data structure to identify a set of nodes that grows upon the user's repeated selection of a particular part of the ink document. At each stage of the selection, the technology highlights a set of elements in the ink document that correspond to the current set of identified nodes. According to another illustrative aspect, the technology produces the data structure by modifying an original data structure provided by a text analysis engine. The technology performs this task with the objective of accommodating structured interaction by the user with the ink document.

A training method and device of an offline handwritten formula recognition model, equipment and a medium, comprising receiving a handwritten formula image containing a handwritten formula, determining a LaTeX label of the handwritten formula, and converting the handwritten formula into a printed formula image according to the LaTeX label; the printed formula image and the handwritten formula image are paired and input into a pre-constructed neural network model for training, the contrast learning loss of the trained neural network model is calculated according to the preset contrast learning loss function, and when it is determined that the contrast learning loss meets the preset condition, the trained neural network model is used as the offline handwritten formula recognition model, thereby using the paired input mode, and guiding the offline handwritten formula recognition model to learn semantic invariant features through the contrast learning loss function, thereby alleviating the influence of large writing style difference on performance and improving the overall recognition accuracy.

A method for generating a brush effect picture, an image editing method, a device, and a storage medium. The method includes: determining a pattern extraction area on a display interface in response to an area selection operation on the display interface of a display (S301); obtaining a first brush effect picture based on the pattern extraction area, where a pattern of the first brush effect picture is a pattern corresponding to the pattern extraction area on the display interface (S302); and obtaining a second brush effect picture based on the first brush effect picture, where the pattern of the first brush effect picture is the same as a pattern of the second brush effect picture, and definition of the second brush effect picture is greater than definition of the first brush effect picture (S303). A brush effect picture obtained by using the method has high definition. This improves user experience.

In some embodiments, an electronic device selects and provides for interaction with handwritten content in a content entry region. In some embodiments, an electronic device generates normalized shapes based on handwritten inputs. In some embodiments, an electronic device identifies actionable text within handwritten content and updates the identified text to be actionable. In some embodiments, an electronic device presents tutorials for text manipulation operations. In some embodiments, an electronic device displays visual feedback of text manipulation operations. In some embodiments, an electronic device selects units of content.

The application discloses a handwriting corner point determination method and device, a storage medium and a terminal, and relates to the technical field of computer image recognition. First, the writing speed of each trajectory point in handwriting and the final curvature of each trajectory point are calculated, and the final curvature is the average of all curvatures of vectors formed by the trajectory point and other trajectory points in a preset range. Then, a trajectory point meeting a condition is selected as a first corner point. Finally, pseudo corner points in the first corner point are removed. Since the average of all curvatures of the trajectory point in the preset range is taken as the final curvature of the trajectory point, that is, the average of the cumulative curvatures of the sliding window is taken as the curvature of the window center trajectory point, the misleading of curvature mutation to corner point selection is reduced, the curvature mutation can be effectively smoothed, more accurate corner points can be selected, line segment fitting of handwriting is performed, pseudo corner points in the first corner point are removed, the pseudo corner points in the corner points can be effectively removed, and finally, correct corner points in the handwriting are determined.

The invention discloses a handwritten electronic signature method and system based on multi-modal biological characteristics. The method comprises the following steps: carrying out real-name authentication on a signer before signing an electronic file; if the real-name authentication is passed, biological characteristic verification and evidence storage are carried out; recording handwriting data after biological feature verification is passed and signing is completed, and forming a data packet; the signed electronic file is printed into digital paper, the data packet is embedded into a digital paper file structure, and the digital paper is a layout file simulating real paper. According to the invention, the problem that the signature of the existing signer cannot represent the real identity and needs to depend on the CA certificate is solved, the tedious processes of applying, issuing, managing and the like for the CA certificate are avoided, and the online signature and the offline signature have the same efficacy.

The application belongs to the field of intelligent human-computer interaction, and relates to a kind of intelligent writing and correcting method and system, comprising: utilizing flexible pressure sensor matrix and coordinate positioning system, the multidimensional data of sensing input pen writing, and constructing four-dimensional writing characteristic space;Adopt three-level correcting system, the logical reasoning verification of answer is carried out through depth semantic understanding model;The three-level correcting system successively includes OCR character shape matching, semantic rectification based on large language model and intelligent correction based on process reward model;Cognitive diagnosis model is introduced, and the degree of hesitation of answering is judged by analyzing answering index;Fusion question solving information, the knowledge point of question is identified using large model, and according to the question answering condition, the weak point of knowledge mastery is inferred;With the intelligent writing capture technology based on multi-modal sensor fusion, the writing information is accurately collected, and the intelligent correcting integrated method is constructed combined with cognitive diagnosis model, the efficient identification and accurate correction of writing content are realized.

A handwritten character beautification method is disclosed. Information about a first handwritten character may be obtained. In addition, a fusion style may be determined based on information about a first level, a handwritten character library, and a target font. The fusion style may indicate a style obtained after the target font and a handwritten font corresponding to a character in the handwritten character library are fused. Finally, a first beautified character corresponding to the first handwritten character is generated based on the information about the first handwritten character and the fusion style. In this way, a handwritten character of a user can be beautified based on the first level selected by the user, and a personal style of the handwritten character of the user can be retained in a finally presented beautification effect of the handwritten character. An electronic device is further disclosed.

A board-writing extraction method and a related device are provided. The board writing extraction method includes: obtaining a target object segmentation image of a writing-board image, wherein a contrast ratio between a target object and a non-target object in the target object segmentation image reaches a predetermined contrast ratio; converting, according to the target object segmentation image, a grayscale image of the writing-board image into a to-be-processed grayscale image with the board writing being highlighted; and performing binarization processing on the to-be-processed grayscale image to obtain a board-writing image of the writing-board image. The described method and related device can effectively reduce the noise in the board-writing image extracted from the writing-board image.

Systems and methods for providing answers to digitally inked questions within an electronic document. One system includes an electronic processor configured to receive detected interactions between a touchscreen and a digital pen representing one or more digital strokes within a canvas and determine whether the one or more digital strokes match a predetermined signifier. The electronic processor is also configured to, in response to determining that the one or more digital strokes match the predetermined signifier, determine a digital ink instance included in the canvas based on a position of the one or more digital strokes within the canvas, extract text from the digital ink instance, submit the extracted text to an answer service as a query, receive a text-based answer to the query from the answer service, convert the text-based answer to digital ink, and add the digital ink to the canvas.

In some examples, systems and methods for formatting ink are provided. Ink stroke data may be received, letters may be identified from the ink stroke data, and spacing may be identified between the letters. A user command and semantic context may be received. An action may be determined based on the user command, the semantic context, and the spacing between the letters. Further, the action may be performed.

Provided is a pen data storage apparatus that stores pen data including at least coordinate data regarding a position instructed and input by an electronic pen, in units of the pen data, in which each piece of the stored pen data includes function identification information that identifies a processing function for the pen data.

An electronic device may: receive an input for searching for note contents stored in a memory, the input may include at least one of a piece of text input through a virtual keyboard or a path of an external object dragged on a display; determine, based on identifying a first piece of text from an input, one or more second pieces of text, which appear to be related to the first piece of text, using a language model representing a relationship between words; determine feature information of one or more paths based on identifying the one or more paths from the input; execute a function for searching a database related to note contents using at least one of the one or more second pieces of text and the feature information; and display a screen providing at least one note content on the display in response to at least one note content matched with the input, identified based on the execution of the function.

The invention discloses a non-contact handwriting device based on an ultraviolet specific image sensor and a preparation method, and relates to the technical field of image sensing and human-computer interaction. Comprising a glass substrate 1, a transparent thin film transistor array 2 formed on the substrate, a photosensitive material layer 3 formed on the thin film transistor array, and an ultraviolet curing adhesive packaging layer 4 covering the photosensitive material layer. A light-sensitive material is a Bi2S3 material with a wavelength selective light current memory characteristic, generates long-time light current maintenance based on a photo-thermal electric effect in an ultraviolet band, rapidly attenuates in visible to near-infrared bands, and has ultraviolet input selectivity and ambient light anti-interference capability. According to the system, an ultraviolet laser pen is used as an input tool, handwriting patterns which can be kept for a long time are formed on an image sensor array, and image erasing based on a background normalization mechanism is supported. The image sensor array is composed of synaptic-like devices, has a long-term optical memory characteristic, and realizes integration of perception and storage.

In some embodiments, an electronic device receives handwritten inputs in text entry fields and converts the handwritten inputs into font-based text. In some embodiments, an electronic device selects and deletes text based on inputs from a stylus. In some embodiments, an electronic device inserts text into pre-existing text based on inputs from a stylus. In some embodiments, an electronic device manages the timing of converting handwritten inputs into font-based text. In some embodiments, an electronic device presents a handwritten entry menu. In some embodiments, an electronic device controls the characteristic of handwritten inputs based on selections on the handwritten entry menu. In some embodiments, an electronic device presents autocomplete suggestions. In some embodiments, an electronic device converts handwritten input to font-based text. In some embodiments, an electronic device displays options in a content entry palette.

To provide a technique for determining an angle of view including interrelated descriptions on an information sharing board.SOLUTION: The continuity between a description added to an imaging subject from a frame in which the imaging subject was imaged and a description that has been added to the imaging subject before the description is determined. On the basis of the determination result, descriptions of the imaging subject are divided into groups. On the basis of the time on each group, the angle of view of imaging apparatus is controlled to image the description of the imaging subject of a group to be distributed in the frame.SELECTED DRAWING: Figure 2

The application provides a method for quickly obtaining a highly robust image splicing data set, comprising the following steps: S1, adding text on a blank image: adding text of various gray levels on a blank image; S2, obtaining key points in the blank image: using a SIFT algorithm to extract key points in the image and saving the key point positions for later use; S3, fusing a plurality of background complex images with the blank background image: using a cv2.addWeighted function in an OpenCV library to perform weighted fusion on the background complex images and the blank background image in step S1 to obtain a new fused image; and S4, generating a large number of image splicing training data sets with complex backgrounds and rich key point information by using the cv2.addWeighted fused image and the key point coordinate information formed by the SIFT algorithm in step S2. A large number of complex and highly robust text image splicing training data can be generated in batches by the method.

The present invention relates to a character recognition device, a character recognition method, and a recording medium. The character recognition device is provided with: a touch panel type display section (17) that accepts a character image based on an input operation in a handwriting form; and a control section (21) that performs a recognition process on the accepted character image to derive a first character from the character image, displays the derived first character as a candidate for a character desired by a user on the touch panel type display section (17), and in the touch panel type display section (17), with respect to the first character, can accept a designation operation that indicates a meaning of a character similar to the character desired by the user although it is not the character desired by the user, and the control section (21) further derives a second character having a given association with the first character when the designation operation is accepted, and displays the derived second character as an additional candidate for the character desired by the user on the touch panel type display section (17).

Procedure that includes: Receiving a first handwriting input on a touch-sensitive screen of a device and associating the first handwriting input with an input region of a first user interface appearing on the touch-sensitive screen before changing focus to a second user interface, wherein the first user interface was in focus while receiving the associated user handwriting inputs and wherein the first user interface corresponds to a first unique identifier and the second user interface corresponds to a second unique identifier; Converting the first handwriting input into a first plurality of textually formatted data after changing the focus from the second user interface back to the first user interface; and Entering the textual formatted data into the input area of ​​the first user interface; the method further comprising: after changing the focus from the first user interface to the second user interface, searching a memory area for a stored association between the second identifier and a second handwriting input; and in response to the association being found during the search: Retrieving the second handwriting input from the storage area; and Displaying the retrieved second handwriting input in a handwriting input area of ​​the touch-sensitive screen.

A method, computer program product, and computer system carry out a process of user registration. The process of user registration comprises receiving a registering user input of a drawing process of a drawn image into a graphical user interface and monitoring the first drawing process to gather registration metadata relating to the drawn image. The process also comprises providing, by applying an artificial intelligence image analysis method, a description of the drawn image. Further, the process comprises securely storing the description and a set of the registration metadata for later authentication by comparison to a description of a later drawn image and metadata relating to the later drawn image.

The invention provides a dynamic handwriting enhancement method and device based on learning content semantic analysis and electronic equipment, and relates to the technical field of electronic note processing, and the method comprises the steps: obtaining space-time dynamic data corresponding to an original note when a user uses an electronic pen to write; performing stroke unit segmentation on the spatio-temporal dynamic data, and performing feature extraction on the segmented stroke units to obtain a feature tensor of each stroke unit; and performing semantic analysis based on the feature tensor of each stroke unit, determining a target enhancement strategy based on a semantic analysis result, and enhancing the original note based on the target enhancement strategy to obtain an enhanced note. According to the dynamic handwriting enhancement method and device based on learning content semantic analysis and the electronic equipment, by understanding the knowledge essence of the written content, sensing the real-time cognitive state of the user and applying the optimization strategy matched with the knowledge type, the handwriting readability is remarkably improved, and meanwhile the personal writing style is strictly reserved.

The embodiment of the invention provides a handwriting system and a handwriting recognition method. The handwriting system comprises a writing surface, a handwriting pen and an electronic device, the writing surface comprises at least one optical identification code, and each optical identification code corresponds to one coordinate; the handwriting pen is used for determining a written target content according to a plurality of target optical identification codes passed by a user when the user uses the handwriting pen to write on the writing surface; the electronic device comprises a display screen, and the display screen is used for displaying the target content. According to the scheme, the writing track when the user writes the characters can be identified through the optical identification code on the writing surface, and the writing track can assist in identifying the written characters, so that the accuracy of handwriting identification is improved.