A processing method includes obtaining input information from an input member of an electronic apparatus. The input information includes a behavior parameter in a process of inputting a target word. The method further includes determining a display parameter of the target word based on the behavior parameter to display the target word on a target display according to the display parameter. The display parameter represents feature information when inputting the target word.

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.

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.

According to the present disclosure, a handwriting image and a background image are combined, thereby generating a combined image, a correct answer label image is generated based on the handwriting image, and the generated combined image and the generated correct answer label image are used as learning data for training a neural network.

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.

This invention adds a touch screen to a hardware keyboard as additional input from handwriting for a PC or a laptop. The new keyboard with a small size touch screen provides convenient ways for handwriting notes, drawing graph or painting, converting handwritten characters to formal text. The new keyboard with a large size touch screen can work with a PC/laptop as a PC/laptop with the advantage of input from touch screen over traditional PC/laptop. The new keyboard with a large size touch screen can work with a PC/laptop as a tablet with the advantage of input from hardware keys and mouse over a regular tablet. The new keyboard with a large size touch screen can work with a PC/laptop as a PC/laptop and a tablet simultaneously, either PC/laptop or tablet with the advantage of additional said input ways over a traditional PC/laptop or a tablet. An optional fingerprint sensor provides fingerprint for security application, e.g., account login, document sign and identity verification.

Method, computer readable medium, and apparatus of recognizing character zone in a digital document. In an embodiment, the method includes classifying a segment of the digital document as including text, calculating at least one parameter value associated with the classified segment of the digital document, determining, based on the calculated at least one parameter value, a zonal parameter value, classifying the segment of the digital document as a handwritten text zone or as a printed text zone based on the determined zonal parameter value and a threshold value, the threshold value being based on a selection of an intersection of a handwritten text distribution profile and a printed text distribution profile, each of the handwritten text distribution profile and the printed text distribution profile being associated with a zonal parameter corresponding to the determined zonal parameter value, and generating, based on the classifying, a modified version of the digital document.

An electronic device is disclosed. The electronic device comprises a storage unit for storing a training model of a multi-dimensional long short-term memory (MDLSTM), and a processor for acquiring an image including at least one of handwritten text and printed text, identifying each text line region in the image through image processing, and recognizing text included in the each identified text line region, on the basis of the training model.

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.

In some implementations, a device may receive an image that depicts handwritten text. The device may determine that a section of the image includes the handwritten text. The device may analyze, using a first image processing technique, the section to identify subsections of the section that include individual words of the handwritten text. The device may reconfigure, using a second image processing technique, the subsections to create preprocessed word images associated with the individual words. The device may analyze, using a word recognition model, the preprocessed word images to generate digitized words that are associated with the preprocessed word images. The device may verify, based on a reference data structure, that the digitized words correspond to recognized words of the word recognition model. The device may generate, based on verifying the digitized words, digital text according to a sequence of the digitized words in the section.