A method for recognizing online handwriting comprising acquiring, by a handwriting instrument comprising a module comprising at least one motion sensor, motion data on the handwriting of the user when the user is writing a sequence of characters with the handwriting instrument, the handwriting instrument further including a body extending longitudinally between a first end and a second end, the first end having a writing tip which is able to write on a support, analyzing the motion data with a machine learning model trained in a multi-task way, the machine learning model being configured to deliver as an output the sequence of characters which was written by the user with the handwriting instrument.

A handwriting processing method, a handwriting processing device and a non-transitory storage medium. The handwriting processing method includes: acquiring a handwriting point group corresponding to a stroke on a working surface of a touch device, the handwriting point group including a plurality of handwriting points arranged in sequence, and data of each handwriting point in the plurality of handwriting points including a coordinate and an action type, determining a plurality of model patterns corresponding to the plurality of handwriting points of model patterns being in one-to-one correspondence with the plurality of handwriting points; and sequentially connecting the plurality of model patterns, to determine a handwriting track for displaying corresponding to the handwriting point group.

A method and a system for converting a font of a Chinese character in an image, a computer device and a medium are disclosed. A specific implementation of the method includes: acquiring a stroke of a to-be-converted Chinese character in the image and spatial distribution information of the stroke; and generating a Chinese character in a target font that corresponds to the to-be-converted Chinese character in the image according to the stroke of the to-be-converted Chinese character, the spatial distribution information of the stroke and standard stroke information of the target font, to replace the to-be-converted Chinese character.

One variation of a method includes: accessing a handwriting sample comprising a set of user glyphs handwritten by a user; for each character in a set of characters, identifying a subset of user glyphs corresponding to the character in the handwriting sample, characterizing variability of a set of spatial features across the subset of user glyphs, and storing variability of the set of spatial features across the subset of user glyphs in a character container corresponding to the character; and compiling the set of character containers into a handwriting model for the user. The method further includes: accessing a text string comprising a combination of characters in the set of characters; for each instance of each character in the text string, inserting a set of variability parameters into the handwriting model to generate a synthetic glyph representing the character; and assembling the set of synthetic glyphs into a print file.

Ink-processing technology is set forth herein for detecting a gesture that a user performs in the course of interacting with an ink document. The technology operates by identifying a grouping of ink strokes created by the user. The technology then determines whether the grouping expresses a gesture based on a combination of spatial information and image information, both of which describe the grouping. That is, the spatial information describes a sequence of positions traversed by the user in drawing the grouping of ink strokes using an ink capture device, while the image information refers to image content in an image produced by rendering the grouping into image form. The technology also provides a technique for identifying the grouping by successively expanding a region of analysis, to ultimately provide a spatial cluster of ink strokes for analysis.

A handwriting processing method, a touch display device, a computer device, and a storage medium are provided. The handwriting processing method includes: determining, according to a sample point in a writing track that is input by a user by means of a touch display, a group of track points and a related parameter of each track point, the related parameter including a coordinate parameter and a time parameter; determining, according to the related parameter of each track point, a start position, an end position, and a type of a stroke contained in the writing track, where the type of the stroke includes one of a horizontal stroke, a vertical stroke, a left-falling stroke, a right-falling stroke or a lifting stroke; and determining, according to the type of the stroke, a shape of the start position of the stroke.

The invention concerns a method implemented by a device for displaying strokes of digital ink in a display area and for performing text line extraction to extract text lines from the strokes. In particular, the text line extraction may involve slicing the display area into strips, ordering for each strip the strokes into ordered lists which form collectively a first set of ordered lists, forming for each strip a second set of ordered lists by filtering out from the ordered lists of the first set strokes which are below a given size threshold, and performing a neural net analysis based on said first and second sets to determine for each stroke a respective text line to which it belongs.

A method of processing of a sequence of video frames from a camera capturing a writing surface for subsequent transmission to at least one of a remote videoconferencing client and a remote videoconferencing server. The method comprises receiving the sequence of video frames from the camera; and selecting an image area of interest in the video frames, comprising selecting one of a sub-area of the video frames and an entire area of the video frames. The method also comprises, for each current video frame of the sequence of video frames, generating a pen stroke mask by applying adaptive thresholding to the image area of interest. The method also comprises generating an output video frame using the pen stroke mask. Corresponding systems and computer readable media are disclosed.

Certain embodiments involve automatically controlling modifications to typeface designs. For example, a typeface design application provides a design interface for modifying a design of an input character from a typeface. The typeface design application accesses a machine-learning model that is trained, using multiple training typefaces, to recognize the input character as a reference character. The typeface design application receives, via the design interface, an input modifying the design of the input character. The typeface design application determines that the machine-learning model cannot match the reference character to the input character having a modified design. The typeface design application outputs, via the design interface, an indicator that the input character having the modified design is not recognized as the reference character.

An electronic device and method are disclosed. The electronic device includes a touch-sensitive display, a memory and a processor. The processor implements the method, including: detect a written input including a plurality of strokes through the display, group the plurality of strokes into a first group and a second group based on respective coordinates of each of the plurality of strokes, group first strokes included in the first group into a plurality of blocks, based on a distance between respective coordinates of each of the first strokes, determine a slope for each of the plurality of blocks, rotate an area corresponding to the first group based on the determined slope, execute handwriting recognition on the first strokes based on the rotated area, and displaying a result of the handwriting recognition on the display.