A method and an electronic device for inputting handwriting character are provided. The electronic device comprises a touch screen, a memory, and a processor. The processor is configured to perform the functions of the method. The method comprises steps of: adding a handwriting input on the touch screen; detecting a position of an initial point of the handwriting input; determining an input area for the handwriting input among the plurality of input areas of the touch screen based on the position of the initial point of the handwriting input; determining an operation of the handwriting input based on the position of the initial point of the handwriting input and performing the determined operation; and upon completion of the handwriting input, recognizing the input as a character and displaying the recognized character in the determined input area on the touch screen.

The optical character recognition method applies a first OCR engine to provide an identification of characters of at least a first type of characters and zones of at least a second type of characters in the character string image. A second OCR engine is applied on the zones of the at least second type of characters to provide an identification of characters of a second type of characters. The characters identified by the first OCR engine and by the second OCR engine are in a further step combined to obtain the identification of the characters of the character string image.

Determination of an underlying grid structure that facilitates layout of East Asian text is disclosed. The underlying grid structure includes both a size of character frames and a size of a text block frame. The East Asian text may be obtained from a scan of printed material that has the text formatted according to layout conventions established by the publisher. The text may be reformatted to appear on a display of an electronic device in a manner similar to the formatting in the original scanned document. Reformatting may include reflowing the text in order to fit a greater or lesser number of characters on a line. The reflowing may maintain character spacing from the original document and follow formatting rules against locating certain characters at the start or end of a line.

Systems and associated methodology are presented for Arabic handwriting synthesis including accessing character shape images of an alphabet, determining a connection point location between two or more character shapes based on a calculated right edge position and a calculated left edge position of the character shape images, extracting character features that describe language attributes and width attributes of characters of the character shape images, the language attributes including character Kashida attributes, and generating images of cursive text based on the character Kashida attribues and the width attribues.

An intelligent scoring method and system for a text objective question, the method comprising: acquiring an answer image of a text objective question (101); segmenting the answer image to obtain one or more segmentation results of an answer string to be identified (102); determining whether any of the segmentation results has the same number of characters as the standard answer (103); if no, the answer is determined to be wrong (106); otherwise, calculating identification confidence of the segmentation result having the same number of words as the standard answer, and/or calculating the identification confidence of respective characters in the segmentation result having the same number of words as the standard answer (104); determining whether the answer is correct according to the calculated identification confidence (105). The method can automatically score text objective questions, thus reducing consumption of human resource, and improving scoring efficiency and accuracy.

Ideogram character analysis includes partitioning an original ideogram character into strokes, and mapping each stroke to a corresponding stroke identifier (id) to create an original stroke id sequence that includes stroke identifiers. A candidate ideogram character that has a candidate stroke id sequence within a threshold distance to the original stroke id sequence is selected. One or more embodiments may create new phrase by replacing the original ideogram character with the candidate ideogram character in a search phrase. One or more embodiments perform a search using the search phrase and the new phrase to obtain a result, and present the result. One or more embodiments may replace an original ideogram character in a character recognized document with the candidate ideogram character and store the character recognized document.

A feature extraction system, method and apparatus based on neural network optimization by gradient filtering is provided. The feature extraction method includes: acquiring, by an information acquisition device, input information; constructing, by a feature extraction device, different feature extraction networks, performing iterative training on the networks in combination with corresponding training task queues to obtain optimized feature extraction networks for different input information, and calling a corresponding optimized feature extraction network to perform feature extraction according to a class of the input information; performing, by an online updating device, online updating of the networks; and outputting, by a feature output device, a feature of the input information. The new feature extraction system, method and apparatus avoids the problem of catastrophic forgetting of the artificial neural network in continuous tasks, and achieves high accuracy and precision in continuous feature extraction.

Ideogram character analysis includes partitioning an original ideogram character into strokes, and mapping each stroke to a corresponding stroke identifier (id) to create an original stroke id sequence that includes stroke identifiers. A candidate ideogram character that has a candidate stroke id sequence within a threshold distance to the original stroke id sequence is selected. One or more embodiments may create a new phrase by replacing the original ideogram character with the candidate ideogram character in a search phrase. One or more embodiments perform a search using the search phrase and the new phrase to obtain a result, and present the result. One or more embodiments may replace an original ideogram character in a character recognized document with the candidate ideogram character and store the character recognized document.

The present disclosure relates to systems, methods, and non-transitory computer readable media for generating and providing matching fonts by utilizing a glyph-based machine learning model. For example, the disclosed systems can generate a glyph image by arranging glyphs from a digital document according to an ordering rule. The disclosed systems can further identify target fonts as fonts that include the glyphs within the glyph image. The disclosed systems can further generate target glyph images by arranging glyphs of the target fonts according to the ordering rule. Based on the glyph image and the target glyph images, the disclosed systems can utilize a glyph-based machine learning model to generate and compare glyph image feature vectors. By comparing a glyph image feature vector with a target glyph image feature vector, the font matching system can identify one or more matching glyphs.

A texture recognition device and an operation method of a texture recognition device are provided. The texture recognition device includes a light source array and an image sensor array. The light source array includes a plurality of light sources; the image sensor array is at a side of the light source array and includes a plurality of image sensors, and the plurality of image sensors are configured to receive light emitted from the plurality of light sources and reflected to the image sensors by a texture for a texture image collection; each of the image sensors includes a plurality of signal switches, and a signal of each of the image sensors is read through the plurality of signal switches for forming one image pixel of the texture image.