A conversion apparatus is disclosed, including: a storage unit; and a processor configured to perform a conversion process. In the conversion process, a handwriting input for a specific position in a text is received. Conversion candidates for the handwriting input is generated based on context information acquired by analyzing before, after, or around the specific position of the text.

According to certain embodiments, an electronic device may include a display, a memory, and a processor operatively connected to the display and the memory. The processor may be configured to, while receiving user's touch input in a handwriting area of the display, the user's touch input comprising successive input stokes: output the successive input strokes in the handwriting area on the display; determine a first stroke group including some of the successive input strokes, to determine a first character corresponding to the first stroke group, to output the first stroke group in an output area adjacent to the handwriting area on the display, to determine a second stroke group including at least another input stroke received after the some of the successive input strokes, to determine a second character corresponding to the second stroke group, and to output the second stroke group in the output area, move the first stroke group to on one side of the second stroke group on the display.

Techniques are disclosed for generating, utilizing, and validating traceable image CAPTCHAs. In certain embodiments, a traceable image is displayed, and a trace of the image is analyzed to determine whether a user providing the trace is human. In certain embodiments, a computing device receives a request for an image, and in response, creates a traceable image based upon a plurality of image elements. The computing device transmits data representing the traceable image to cause a second computing device to display the traceable image via a touch-enabled display. The computing device receives a user trace input data generated responsive to a trace made at the second computing device, and determines whether the trace is within an error tolerance range of the set of coordinates associated with the traceable image. The computing device then sends a result of the determination.

An information processing system for a handwriting search according to an embodiment of the present invention comprises: an information extraction device for transforming pattern information sensed by an electronic pen into handwriting data on a recording medium with a pattern formed thereon; and an information management device for transforming the handwriting data into digital data, storing the handwriting data and the digital data, searching for digital data including a keyword when a search request including the keyword is received from a user terminal, and transmitting, to the user terminal, the handwriting data corresponding to the searched digital data.

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.

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.

Examples described herein generally relate to systems and methods for handwriting recognition. In an example, a computing device may receive input corresponding to a handwritten word and apply first recognition model to the input. The first recognition model may be configured to determine a first confidence level of a first portion of the input is greater than a second confidence level of a second portion of the input. The computing device may also apply a second recognition model to the input, wherein the second recognition model is different from the first recognition model and combine results of the first recognition model and the second recognition model to determine a list of candidate words. The computing device may also output one or more candidate words from the list of candidate words.

Examples described herein generally relate to systems and methods for handwriting recognition. In an example, a computing device may receive input corresponding to a handwritten word and apply first recognition model to the input. The first recognition model may be configured to determine a first confidence level of a first portion of the input is greater than a second confidence level of a second portion of the input. The computing device may also apply a second recognition model to the input, wherein the second recognition model is different from the first recognition model and combine results of the first recognition model and the second recognition model to determine a list of candidate words. The computing device may also output one or more candidate words from the list of candidate words.