Provided is a data analysis system that generates effective information affecting a user's tendency to buy a product and service. The data analysis system analyzes data to generate information on a tendency of a user, and includes: a memory that stores at least temporarily a plurality of evaluation data to be analyzed; and a controller that evaluates each of the plurality of evaluation data based on training data, wherein the controller extracts first information from the plurality of evaluation data based on results of the evaluation of the plurality of evaluation data, extracts second information from the training data based on a characteristic pattern included in the first information, and generates the information on the tendency of the user from the first information and the second information.

An approach for stroke extraction in free space utilizing a paired ring device is provided. The approach receives one or more images transmitted from the paired ring device, wherein the one or more images are transcribed sequentially from data related to one or more movements recorded by the paired ring device, and wherein the one or more images include one or more of a plurality of vector points, a plurality of coordinates, and a plurality of dots interconnected by a plurality of lines. The approach inputs the one or more images into a character training model. The approach maps the one or more images into one or more characters. The approach transcribes the one or more characters into a digital document.

Techniques are provided for generating a digital image of simulated handwriting using an encoder-decoder neural network trained on images of natural handwriting samples. The simulated handwriting image can be generated based on a style of a handwriting sample and a variable length coded text input. The style represents visually distinctive characteristics of the handwriting sample, such as the shape, size, slope, and spacing of the letters, characters, or other markings in the handwriting sample. The resulting simulated handwriting image can include the text input rendered in the style of the handwriting sample. The distinctive visual appearance of the letters or words in the simulated handwriting image mimics the visual appearance of the letters or words in the handwriting sample image, whether the letters or words in the simulated handwriting image are the same as in the handwriting sample image or different from those in the handwriting sample image.

Disclosed is an Arabic optical character recognition method using Hidden Markov Models and decision trees, comprising: receiving an input image containing Arabic text, removing all diacritics from the input image by detecting a bounding box of each diacritic and comparing coordinates thereof to those of a bounding box of a text body, segmenting the input image into four layers, and conducting feature extraction on the segmented four layers, inputting results of feature extraction into a Hidden Markov Model thereby generating HMM models for representing each Arabic character, conducting iterative training of the HMM models until an overall likelihood criterion is satisfied, and inputting results of iterative training into a decision tree thereby predicting locations and the classes of the diacritics and producing final recognition results. The invention is capable of facilitating simple recognition of Arabic by utilizing writing feature thereof, and meanwhile featuring comparatively high recognition precision.

A system and method for completing a character of a text of a digital document on a computing device, the computing device comprising a processor, a memory, and at least one non-transitory computer readable medium for recognizing input under control of the processor, the at least one non-transitory computer readable medium is configured to cause display (S900) of at least one typeset character of the text on a display interface of the computing device; detect a handwritten input stroke (S902) performed on the digital document in the vicinity of a typeset character; identify an first typeset character (S904) if the typeset character belongs to a list of base characters according to a language model; retrieve a predefined character version (S906) of the first typeset character from the memory; generate a hybrid character (S908) by replacing the initial typeset character by the predefined character; generate a list of character candidates (S910) with associated probabilities of recognition of the hybrid character provided by a recognition expert; select a recognized character (S912) from the character candidate list by using a language expert.

Disclosed in the present invention is a method for decoding Chinese character writing for an invasive brain-computer interface. In a practical application, a corresponding motor neural signal is divided into two states of a writing stroke and a writing stroke break in view of inconsistency of the writing stroke and the writing stroke break during Chinese character writing, and different filters are trained. A hidden markov model (HMM algorithm) and a Viterbi algorithm are used to judge a task state of the motor neural signal, and the corresponding signal is put into a corresponding decoder. The present invention effectively reduces influence of difference of neural data in different states on the decoder, and improves the performance and robustness of the decoder.

Disclosed in the present invention is a method for decoding Chinese character writing for an invasive brain-computer interface. In a practical application, a corresponding motor neural signal is divided into two states of a writing stroke and a writing stroke break in view of inconsistency of the writing stroke and the writing stroke break during Chinese character writing, and different filters are trained. A hidden markov model (HMM algorithm) and a Viterbi algorithm are used to judge a task state of the motor neural signal, and the corresponding signal is put into a corresponding decoder. The present invention effectively reduces influence of difference of neural data in different states on the decoder, and improves the performance and robustness of the decoder.