A display apparatus includes circuitry to receive hand drafted data input by an electronic pen; display, on a screen, a plurality of character string candidates converted in a recognition language from the hand drafted data; and display a converted character string converted from one of the plurality of character string candidates, selected by the electronic pen, into a target language associated with identification information of the electronic pen. The target language is different from the recognition language. In response to selection of the converted character string, the circuitry displays a plurality of character string candidates in the recognition language corresponding to the converted character string.

A display apparatus includes circuitry to receive an operation of changing a direction of display of a character string displayed in a first direction on a display, and control the display to display a converted character string in a second direction corresponding to the operation of changing. The converted character string is converted from the character string into a target language associated with the second direction.

A display apparatus includes circuitry to receive an operation of changing a direction of display of a character string displayed in a first direction on a display, and control the display to display a converted character string in a second direction corresponding to the operation of changing. The converted character string is converted from the character string into a target language associated with the second direction.

A label processing engine receives, as inputs, raw data representative of a label and baseline data, detects a raw data object within the raw data, classifies the raw data object, and localizes the raw data object within the raw data, detects a baseline data object within the baseline data, classifies the baseline data object, and localizes the baseline data object within the baseline data. The engine recognizes corresponding text within the raw data object and the baseline data object and extracts the corresponding text within the raw data object and the baseline data object, reassembles the corresponding text of the raw data object and the baseline data object into respective lines of text, compares the respective lines of text with one another, and issues a notification based on the comparison.

Provided is a training method for a character generation model, a character generation method, apparatus and device, which relate to the technical field of artificial intelligences, particularly, the technical field of computer vision and deep learning. The specific implementation scheme includes: a first training sample is acquired, a target model is trained based on the first training sample, and a first character confrontation loss is acquired; a second training sample is acquired, the target model is trained based on the second training sample, and a second character confrontation loss, a component classification loss and a style confrontation loss are acquired; and a parameter of the character generation model is adjusted according to the first character confrontation loss, the second character confrontation loss, the component classification loss and the style confrontation loss.

An information processing apparatus includes a processor configured to: read plural pieces of character string information written on a form; obtain feature information indicating a feature relating to an arithmetic operation in which numerical information included in the plural pieces of character string information is used and arrangement information indicating a positional relationship between the plural pieces of character string information; and define, on a basis of the feature information and the arrangement information, an arithmetic expression for performing an arithmetic operation using an operator relating to the plural pieces of character string information.

An information processing apparatus includes a processor configured to: read plural pieces of character string information written on a form; obtain feature information indicating a feature relating to an arithmetic operation in which numerical information included in the plural pieces of character string information is used and arrangement information indicating a positional relationship between the plural pieces of character string information; and define, on a basis of the feature information and the arrangement information, an arithmetic expression for performing an arithmetic operation using an operator relating to the plural pieces of character string information.

The present disclosure relates to systems, methods, and non-transitory computer readable media for generating and providing matching fonts by utilizing a glyph-based machine learning model. For example, the disclosed systems can generate a glyph image by arranging glyphs from a digital document according to an ordering rule. The disclosed systems can further identify target fonts as fonts that include the glyphs within the glyph image. The disclosed systems can further generate target glyph images by arranging glyphs of the target fonts according to the ordering rule. Based on the glyph image and the target glyph images, the disclosed systems can utilize a glyph-based machine learning model to generate and compare glyph image feature vectors. By comparing a glyph image feature vector with a target glyph image feature vector, the font matching system can identify one or more matching glyphs.

A device, method, and non-transitory computer readable medium are described. The method includes receiving a dataset including hand written Arabic words and hand written Arabic alphabets from one or more users. The method further includes removing whitespace around alphabets in the hand written Arabic words and the hand written Arabic alphabets in the dataset. The method further includes splitting the dataset into a training set, a validation set, and a test set. The method further includes classifying one or more user datasets from the training set, the validation set, and the test set. The method further includes identifying the target user from the one or more user datasets. The identification of the target user includes a verification accuracy of the hand written Arabic words being larger than a verification accuracy threshold value.

A device, method, and non-transitory computer readable medium are described. The method includes receiving a dataset including hand written Arabic words and hand written Arabic alphabets from one or more users. The method further includes removing whitespace around alphabets in the hand written Arabic words and the hand written Arabic alphabets in the dataset. The method further includes splitting the dataset into a training set, a validation set, and a test set. The method further includes classifying one or more user datasets from the training set, the validation set, and the test set. The method further includes identifying the target user from the one or more user datasets. The identification of the target user includes a verification accuracy of the hand written Arabic words being larger than a verification accuracy threshold value.