A handwriting input apparatus that displays stroke data handwritten based on a position of an input unit contacting a touch panel, includes circuitry configured to implement a handwriting recognition control unit for recognizing stroke data and converting the stroke data into text data, and an authentication control unit for authenticating a user based on the stroke data, and a display unit for displaying a display component for receiving a signature together with the text data when the authentication control unit determines that a user has been successfully authenticated.

In some embodiments, a method includes training a first machine learning model based on multiple documents and multiple templates associated with the multiple documents. The method further includes executing the first machine learning model to generate multiple relevancy masks, the multiple relevancy masks to remove a visual structure of the multiple templates from a visual structure of the multiple documents. The method further includes generating multiple multichannel field images to include the multiple relevancy masks and at least one of the multiple documents or the multiple templates. The method further includes training a second machine learning model based on the multiple multichannel field images and multiple non-native texts associated with the multiple documents. The method further includes executing the second machine learning model to generate multiple non-native texts from the multiple multichannel field images.

A method and system for the spotting of keywords in a handwritten document, the method comprising the steps of inputting an image of the handwritten document, performing word segmentation on the image to obtain segmented words, performing word matching, and outputting the spotted keywords. The word matching itself consisting in the substeps of performing character segmentation on the segmented words, performing character recognition on the segmented characters, performing distance computations on the recognized characters using a Generalized Hidden Markov Model with ergodic topology to identify words based on character models and performing nonkeyword rejection using a classifier based on a combination of Gaussian Mixture Models, Hidden Markov Models and Support Vector Machines.

Systems and associated methodology are presented for Arabic handwriting synthesis including accessing character shape images of an alphabet, determining a connection point location between two or more character shapes based on a calculated right edge position and a calculated left edge position of the character shape images, extracting character features that describe language attributes and width attributes of characters of the character shape images, the language attributes including character Kashida attributes, and generating images of cursive text based on the character Kashida attribues and the width attribues.

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.

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.

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 extracting strokes in a free space environment is described. Boundaries are displayed in a free space environment describing at least one two-dimensional surface area. One or more language movements are extracted from the free space environment by a paired ring device and transmitted as images for processing. Haptic feedback is provided to the paired ring device in response to detecting at least one language movement occurring outside of at least one two-dimensional surface area. At least one extracted language movement is input into a character training model.

A system and method for detecting, identifying, and displaying handwriting-based entry is provided. The system and method include features for detecting entry of at least one first letter based on handwriting, identifying a style of the at least one first letter, and displaying at least one second letter associated with the at least one first letter based on, and in the form of, the identified style of the at least one first letter.

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.