A user terminal apparatus includes an input receiver, an intersecting-image information obtaining unit and a divider. The input receiver receives input information relating to image-drawing and image-erasing. The intersecting-image information obtaining unit obtains image-drawing input information intersecting image-erasing input information as intersecting-image information when the input receiver receives image-erasing input information. The divider divides the intersecting-image information to erase a portion of the intersecting-image information contained in an erasing area computed from the image-erasing input information when the input information is image-erasing input information.

Aspects of the present disclosure provide a gesture recognition method and an apparatus for capturing gesture. The apparatus categorizes the raw data of a gesture into gesture elements, and utilizes the contextual dependency between the gesture elements to perform gesture recognition with a high degree of accuracy and small data size. A gesture may be formed by a sequence of one or more gesture elements.

A method of providing a handwriting style correction function and an electronic device adapted to the method are provided. The electronic device includes: a touch screen; a processor electrically connected to the touch screen; and a memory electrically connected to the processor. The memory stores instructions which, when executed by the processor, cause the processor to perform operations comprising: displaying at least one reference character on the touch screen; receiving a touch gesture via the touch screen; displaying a track of the received touch gesture on the touch screen; recognizing the track of the touch gesture as at least one input character corresponding to at least one reference character; identifying at least one reference character and at least one input character, as at least one stroke, based on a preset standard; comparing corresponding strokes of at least one reference character with at least one input character, and determining errors by strokes; and summing the errors by strokes of each of at least one reference character, and determining errors by characters.

A method for detecting handwriting problem, comprising: acquiring, by a handwriting instrument comprising one motion sensor, motion data while a user is using the handwriting instrument, analyzing the motion data by an artificial intelligence trained to detect a handwriting problem.

An electronic communications method includes receiving, by a computing device, electronic information, with the electronic information including handwritten text. The electronic communications method includes analyzing, by the computing device, the electronic information, with the analyzing includes analyzing the handwritten text. The electronic communications method includes generating printed text based on analyzing the handwritten text. The electronic communications method includes generating a converted document with the printed text based on the electronic information.

A system for detecting handwriting problems may include a handwriting instrument 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, the handwriting instrument further including at least one motion sensor configured to acquire data on the handwriting of the user when a user is using the handwriting instrument, and one calculating unit communicating with the motion sensor and configured to analyze the data by an artificial intelligence trained to detect whether the user has handwriting problems.

One aspect of the present disclosure relates to a method of sentiment analysis based on ambiguity analysis, which includes analyzing information with the sentiment analysis models and the ambiguity analysis models. Another aspect of the present disclosure relates to a method of training the sentiment analysis models and ambiguity analysis models, which includes acquiring information, constructing lexicons, conducting sentiment analysis and ambiguity analysis with said lexicons, acquiring corpus, and training models, etc. Meanwhile, another aspect of the present disclosure relates to a system of sentiment analysis, which includes input, and output modules, acquisition modules, processing modules and database.

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.

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.

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.