An information processing apparatus having a display apparatus to display information includes a specified position detector configured to detect a specified position on a display surface displaying information, a visual information generator configured to generate visual information based on the specified position detected by the specified position detector, a symbol recognizer configured to recognize a symbol formed of one or more visual information items generated by the visual information generator and determine recognition process candidates of the symbol, and a saving unit configured to save the symbol and the recognition process candidates as a file in a storage.

A system and method for enabling users to draw emojis for insertion into electronic text-based messages are disclosed. The system receives a handwritten drawing input from a user composing an electronic text based message on a computing device. The handwritten drawing input is to represent an emoji for insertion into the message, and comprises a series of strokes input to the computing device by the user. The system analyzes the series of strokes and matches the analyzed series of strokes to at least one emoji in a set of emojis. The user can then select the at least one emoji for insertion into the message.

The electronic devices described herein are configured to enhance user experience associated with drawing or otherwise inputting shape data into the electronic devices. Shape input data is identified and matched against known shape patterns and, when a match is found, an entity associated with the shape is determined. The entity is converted into an annotation for rendering and/or displaying to the user. The shape identification, entity determination, and annotation conversion may all be based on one or more context elements to increase the accuracy of the shape interpretation. In particular, elements of conversations held via the electronic devices may be used as context for the shape interpretation. Further, machine learning techniques may be applied based on a variety of feedback data to improve the accuracy, speed, and/or performance of the shape interpretation process.

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.

Certain aspects of the present methods and systems may focus on computer implemented methods of obtaining digitized hand-writing data corresponding to a sample of a needed code point of a set of code points. Such methods may include: obtaining a sample of digitized handwritten text, the sample of digitized handwritten text including glyph data corresponding to a first glyph, the first glyph corresponding to the needed code point of the set of code points; associating the first glyph with the needed code point; identifying stroke data in the glyph data, the stroke data corresponding to a stroke component of the first glyph, determining a plurality of dimensional values of the stroke component in the stroke data; and associating the plurality of dimensional values with a new code point sample of the needed code point in a code point set data structure.

A first handwriting input is received comprising strokes corresponding to a set of first characters comprising one or more first characters forming a first language model unit. A set of candidate first characters and a set of candidate first language model units with corresponding probability scores are determined based on an analysis of the one or more sets of candidate first characters using the first language model and a corresponding first character recognition model. When no first probability score satisfies a threshold, one or more sets of candidate second characters and a set of candidate second language model units are determined based on an analysis of the first handwriting input using a second language model and a corresponding second character recognition model. A first candidate list is then output comprising at least one of the set of candidate second language model units.

Improved accuracy and user interaction efficiency for selecting a grouping of digital strokes is provided. In response to receiving an indication of a selection input on or in proximity to a digital stroke, a determination is made as to whether the digital stroke is part of an existing group of digital strokes. When the digital stroke is not part of an existing group, an analysis of the digital stroke and other digital strokes within a calculated boundary is performed for determining which strokes should be included in a stroke grouping. A stroke grouping is generated based on the determination. Accordingly, in response to the selection input on or in proximity to the digital stroke, the selection is expanded to the stroke grouping, thus improving the accuracy of the selection gesture and improving computer efficiency.

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.

A method of receiving input characters with a computer is disclosed The computer executes a process which includes receiving, on a display image screen for displaying handwritten characters, a selection instruction with respect to a handwritten certain character; displaying, on the display image screen, a candidate display image screen for displaying a correction candidate with respect to the certain character, in response to a reception of the selection instruction, a display area for the candidate display image screen extending at least to a display area for the certain character, and performing a correction process for the certain character in response to a handwritten input with respect to the candidate display image screen.