According to certain embodiments, an electronic device may include a display, a memory, and a processor operatively connected to the display and the memory. The processor may be configured to, while receiving user's touch input in a handwriting area of the display, the user's touch input comprising successive input stokes: output the successive input strokes in the handwriting area on the display; determine a first stroke group including some of the successive input strokes, to determine a first character corresponding to the first stroke group, to output the first stroke group in an output area adjacent to the handwriting area on the display, to determine a second stroke group including at least another input stroke received after the some of the successive input strokes, to determine a second character corresponding to the second stroke group, and to output the second stroke group in the output area, move the first stroke group to on one side of the second stroke group on the display.

Methods and systems are provided for end-to-end text recognition in digitized documents of handwritten characters over multiple lines without explicit line segmentation. An image is received. Based on the image, one or more feature maps are determined. Each of the one or more feature maps include one or more feature vectors. Based at least in part on the one or more feature maps, one or more scalar scores are determined. Based on the one or more scalar scores, one or more attention weights are determined. By applying the one or more attention weights to each of the one or more feature vectors, one or more image summary vectors are determined. Based at least in part on the one or more image summary vectors, one or more handwritten characters are determined.

A display device includes a circuitry configured to perform a search for a plurality of image candidates in an image transformation dictionary part, based on handwritten data, and a display configured to display the plurality of image candidates obtained by the search. At least a portion of the plurality of image candidates displayed on the display represents a different person or an object.

Examples described herein generally relate to systems and methods for handwriting recognition. In an example, a computing device may receive input corresponding to a handwritten word and apply first recognition model to the input. The first recognition model may be configured to determine a first confidence level of a first portion of the input is greater than a second confidence level of a second portion of the input. The computing device may also apply a second recognition model to the input, wherein the second recognition model is different from the first recognition model and combine results of the first recognition model and the second recognition model to determine a list of candidate words. The computing device may also output one or more candidate words from the list of candidate words.

The present disclosure is generally directed to systems and methods for executing optical character recognition faster than at least some traditional OCR systems, without sacrificing recognition accuracy. Towards this end, various exemplary embodiments involve the use of a bounding box and a grid-based template to identify certain unique aspects of each of various characters and/or numerals. For example, in one embodiment, the grid-based template can be used to recognize a numeral and/or a character based on a difference in centerline height between the numeral and the character when a monospaced font is used. In another exemplary embodiment, the grid-based template can be used to recognize an individual digit among a plurality of digits based on certain parts of the individual digit being uniquely located in specific portions of the grid-based template.

The invention relates to a method implemented by a computing device for processing math and text in handwriting, comprising: identifying symbols by performing handwriting recognition on a plurality of strokes; classifying, as a first classification, first symbols as either a text symbol candidate or a math symbol candidate with a confidence score reaching a first threshold; classifying, as a second classification, second symbols other than first symbols as either a text symbol candidate or a math symbol candidate with a respective confidence score by applying predefined spatial syntactic rules; updating or confirming, as a third classification, a result of the second classification by establishing semantic connections between symbols and comparing the semantic connections with the result of the second classification; and recognising each symbol as either text or math based on a result of said third classification.

One aspect of the present disclosure relates to a method of sentiment analysis based on ambiguity analysis, which includes analyzing information with the sentiment analysis models and the ambiguity analysis models. Another aspect of the present disclosure relates to a method of training the sentiment analysis models and ambiguity analysis models, which includes acquiring information, constructing lexicons, conducting sentiment analysis and ambiguity analysis with said lexicons, acquiring corpus, and training models, etc. Meanwhile, another aspect of the present disclosure relates to a system of sentiment analysis, which includes input, and output modules, acquisition modules, processing modules and database.

A method for motion tracking and text recognition, the method including a step of generating ultrasound waves with a transmitter; a step of receiving the ultrasound waves at a receiver, the receiver including sensors that record the ultrasound waves; a step of estimating with a processor, angle-of-arrival information for the ultrasound waves; a step of associating the angle-of-arrival information with a gesture; a step of extracting features from the gesture; and a step of classifying the gesture as a specific text character based on the extracted features by comparing the extracted features with known text characters stored in one or more templates.

An information processing system includes circuitry that receives position information of first stroke information and second stroke information drawn on a display at different timing, adds additional information indicating a relationship of the received first stroke information and the received second stroke information to the received first stroke information, and generates, based on the position information of the received first stroke information, the position information of the received second stroke information and the added additional information, consecutive data used for displaying (playing) information drawn on the display as the first stroke information and the second stroke information, and a memory that stores the generated consecutive data.

A method for structural analysis and recognition of a handwritten mathematical formula in a natural scene image, including: transforming a gray matrix of a natural scene image into a local contrast matrix, and performing a binary division to the obtained local contrast matrix using an Otsu method, thereby obtaining a binary matrix; performing a connected domain analysis to the binary matrix, eliminating non-character connected domains to obtain character connected domains; performing a detection of elements of a special structure of a formula to the character connected domains using a correlation coefficient method, and separately annotating all the detected elements of the special structure: dividing rows of the binary matrix by means of horizontal projection; recognizing each character connected domain by means of a convolutional neural network; defining an output sequence, and outputting the results of recognition in a corresponding sequence according to a typesetting format of latex.