A method for automatically selecting one of a plurality of recognition algorithms for a handwritten input of alphanumeric characters and/or gestures into a selected input field displayed on a screen using a touch-sensitive input apparatus comprises carrying out optical character recognition in a region of the screen which comprises at least the input field and the immediate environment of the input field, or carrying out voice recognition for a voice instruction acoustically output after the selected input field has been displayed. Terms describing field types are searched for in the result of the optical character recognition or the voice recognition, and a recognition algorithm which is adapted to a field type found in the result of the optical character recognition or the voice recognition is selected.

Methods, systems, and computer-readable media related to a technique for providing handwriting input functionality on a user device. A handwriting recognition module is trained to have a repertoire comprising multiple non-overlapping scripts and capable of recognizing tens of thousands of characters using a single handwriting recognition model. The handwriting input module provides real-time, stroke-order and stroke-direction independent handwriting recognition for multi-character handwriting input. In particular, real-time, stroke-order and stroke-direction independent handwriting recognition is provided for multi-character, or sentence level Chinese handwriting recognition. User interfaces for providing the handwriting input functionality are also disclosed.

A system, method and computer program product for hand-drawing diagrams including text and non-text elements on a computing device are provided. The computing device has a processor and a non-transitory computer readable medium for detecting and recognizing hand-drawing diagram element input under control of the processor. Display of input diagram elements in interactive digital ink is performed on a display device associated with the computing device. One or more of the diagram elements are associated with one or more other of the diagram elements in accordance with a class and type of each diagram element. The diagram elements are re-displayed based on one or more interactions with the digital ink received and in accordance with the one or more associations.

Methods, systems, and computer-readable media related to a technique for providing handwriting input functionality on a user device. A handwriting recognition module is trained to have a repertoire comprising multiple non-overlapping scripts and capable of recognizing tens of thousands of characters using a single handwriting recognition model. The handwriting input module provides real-time, stroke-order and stroke-direction independent handwriting recognition for multi-character handwriting input. In particular, real-time, stroke-order and stroke-direction independent handwriting recognition is provided for multi-character, or sentence level Chinese handwriting recognition. User interfaces for providing the handwriting input functionality are also disclosed.

Provided is a method for recognizing handwritten characters in a terminal through federated learning. In the method, a first common prediction model for recognizing text from handwritten characters input from a user is applied, the handwritten characters are received from the user, feature values are extracted from an image including the handwritten characters, the feature values are input to the first common prediction mode, first text information is determined from an output of the first common prediction model, the first text information and a second text information received from the user for error correction of the first text information are cached, and the first common prediction model is learned using the image including the handwritten characters, the first text information, and the second text information. In this way, the terminal can determine the text from the handwritten characters input by the user, and can learn the first common prediction model through a feedback operation of the user.

Systems and methods for shape prediction for digital inking applications include training a shape prediction model to predict complete shapes based digital ink data defining unfinished shapes. During use, digital ink data representing an unfinished shape is input to a digital inking application and displayed in a canvas area of the application. The digital ink data is also provided to the shape prediction model as input. The shape prediction model generates a shape prediction based on the digital ink data. The shape prediction is displayed in the canvas area. When a second input is received indicating acceptance of the shape prediction, the digital ink forming the unfinished shape is replaced with digital ink forming a predicted complete shape.

An interaction method, an electronic device and a computer storage medium. The interaction method includes: acquiring a trajectory point set input by a user, wherein the trajectory point set at least includes one trajectory point; determining a candidate object according to the trajectory point set, wherein the candidate object includes a first candidate object and a second candidate object; displaying a first selection icon for representing the first candidate object and a second selection icon for representing the second candidate object; and displaying the first candidate object or executing an operation corresponding to the first candidate object in response to the user's selection operation on the first selection icon, and displaying the second candidate object or executing an operation corresponding to the second candidate object in response to the user's selection operation on the second selection icon.

A system and method for correcting image data is disclosed. The method includes receiving one or more documents from one or more electronic mediums. The method further includes determining a primary character and one or more alternate characters corresponding to the mis-captured character image, extracting one or more confident instances of the primary character and the one or more alternate characters from the one or more documents and generating one or more scores corresponding to the primary character and the one or more alternate characters. Further, the method includes predicting a correct character corresponding to the mis-captured character image by using a trained image prediction-based ML model and automatically replacing the mis-captured character image with the predicted correct character.

Disclosed are an electronic device and method for displaying related information of parsed data. The method includes receiving an input of selecting one of at least one piece of parsed data displayed on a display, analyzing a data type of the selected parsed data, identifying an application corresponding to the analyzed data type, searching a database of the identified application for information related to the selected parsed data, and displaying the searched information on the display.

A method and apparatus for an auto-complete function in a user's handwriting for touch screen devices is described. In one embodiment, the method includes receiving, from a touch-screen display, a handwriting input. The handwriting input is converted into textual information. Based on the textual information, one or more prompt options are generated. The one or more prompt options are rendered for display on the touch-screen display in a similar appearance as the handwriting input. The method can further include receiving a selected prompt option input and rendering the selected prompt option for display on the touch screen display in a similar appearance as the handwriting input.