Provided is an input feedback-based smart pen including: a main body having a shape that enables manipulation of a user; an input unit connected to the main body and performing an input operation by a manipulation of the user; an input information recognition unit configured to recognize information input by a user through the manipulation of the input unit; and an expression unit including one or more expression members formed in an area of an outer surface of the main body to be detectable by the user to express information corresponding to input information recognized by the input information recognition unit.

A “Stroke Untangler” composes handwritten messages from handwritten strokes representing overlapping letters or partial letter segments are drawn on a touchscreen device or touch-sensitive surface. These overlapping strokes are automatically untangled and then segmented and combined into one or more letters, words, or phrases. Advantageously, segmentation and composition is performed without requiring user gestures, timeouts, or other inputs to delimit characters within words, and without using handwriting recognition-based techniques to guide untangling and composing of the overlapping strokes to form characters. In other words, the user draws multiple overlapping strokes. Those strokes are then automatically segmented and combined into one or more corresponding characters. Text recognition of the resulting characters is then performed. Further, the segmentation and combination is performed in real-time, thereby enabling real-time rendering of the resulting characters in a user interface window. A related drawing mode enables entry of drawings in combination with the handwritten characters.

According to an embodiment, an electronic device may include a display, a memory, and at least one processor operatively coupled to the display and the memory. The memory stores instructions that are configured to, when executed, enable the at least one processor to display first stroke data of a first stroke trajectory, based on an input of a first touch-move after a first touch-down on the display, perform shape recognition on the first stroke trajectory, based on a size of the first stroke trajectory and a distance between a stroke start point of the first stroke trajectory and another point on the first stroke trajectory, convert the first stroke data into second stroke data corresponding to a recognized shape form, based on the shape recognition, and display the second stroke data on the display. Various other embodiments may be possible.

An input method based on visual recognition, performed at a processor, includes: obtaining frames of gesture images; recognizing a size and a position of a hand feature in each frame; determining whether a variation amount in the size of the hand feature in the frame as compared to a preset calibration size is less than a preset threshold value; causing a position of the hand feature in the frame to be stored as a handwriting point; obtaining at least one stroke trace, according to handwriting points stored, each including handwriting points stored in sequence according to positions of hand features in at least two consecutive frames; and recognizing the at least one stroke trace and obtaining a target object corresponding to the at least one stroke trace.

An information processing device includes an acquisition unit configured to acquire detection data by processing a first detection result output from a first sensor configured to detect at least a feature of a user in a nearby region near a display device and to acquire position data by processing a second detection result output from a second sensor configured to detect a position of at least a part of an object in a display image displayed by the display device; and an identification unit configured to identify a user who would have written the object onto the display image by using the detection data and the position data.

An electronic device includes an input configured to receive a signature from a user; a communication interface configured to communicate with a server; and a controller configured to classify the signature into at least one stroke, to transmit authentication information for the at least one stroke to the server, and to control the communication interface to receive a result of authentication of the signature from the server.

The present disclosure relates to methods of analyzing works of art for purposes of authentication or attribution. Such methods may be implemented by receiving digital image data associated with a work of art, identifying a plurality of artist's strokes formed along a surface of the work of art, segmenting the plurality of strokes into a plurality of individual strokes, analyzing the plurality of individual strokes to determine stroke characteristics, and comparing the stroke characteristics to stroke characteristics derived from one or more computational models based on known works of art.

A system for processing a handwritten message received by a messaging app can modify playback over time of the handwritten message based on a set of rules to determine how to modify the playback. In one embodiment, graphics data (e.g. vector graphics data) that represents the handwritten message is processed using the rules to determine how to modify the playback such that the resulting playback includes at least one modification over a portion of time of the playback.

In embodiments of the invention, a compression system for a mobile messaging application is disclosed in which messages are received over a communication link in what appears to be the sending user's personal handwriting.

Technology is descried herein for parsing an ink document having a plurality of ink strokes. The technology performs stroke-level processing on the plurality of ink strokes to produce stroke-level information, the stroke-level information identifying at least one characteristic associated with each ink stroke. The technology also performs object-level processing on individual objects within the ink document to produce object-level information, the object-level information identifying one or more groupings of ink strokes in the ink document. The technology then parses the ink document into constituent parts based on the stroke-level information and the object-level information. In some implementations, the technology converts the ink stroke data into an ink image. The stroke-level processing and/or the object-level processing may operate on the ink image using one or more neural networks. More specifically the stroke-level processing can classify pixels in the input image, while the object-level processing can identify bounding boxes containing possible objects.