Methods and supporting systems for representing and searching characters, comprising: obtaining an image of the character, labelling a structure of the character by defining a plurality of nodes and a plurality of edges on the character in the image, and generating a representation of the character by extracting a set of two-dimensional coordinates to represent the plurality of nodes and by extracting a matrix to represent the plurality of edges, and providing the representation in a searchable database.

An information processing apparatus includes: a determiner that determines an area including a handwritten figure from image data; a recognizer that recognizes a handwritten character from the handwritten figure; an acquirer that acquires a file name; and a file generator that generates a file with a file name based on a handwritten character when the recognizer recognizes the handwritten character based on the image data and generates a file with the file name acquired by the acquirer when the recognizer does not recognize a handwritten character.

The present disclosure relates to a font detection method using a neural network. The font detection method using the neural network according to the present disclosure includes receiving a target text image including a text; resizing a horizontal or vertical size to a reference input size according to an aspect ratio of the input target text image; and inputting the resized target text image into a trained neural network and outputting a font of the text included in the text image, and the neural network may be trained with a unit image extracted as a unit region of the reference input size from a training image generated by synthesizing a background with the text. According to the present disclosure, fonts according to various usage examples may be effectively detected.

A method and a system for converting a font of a Chinese character in an image, a computer device and a medium are disclosed. A specific implementation of the method includes: acquiring a stroke of a to-be-converted Chinese character in the image and spatial distribution information of the stroke; and generating a Chinese character in a target font that corresponds to the to-be-converted Chinese character in the image according to the stroke of the to-be-converted Chinese character, the spatial distribution information of the stroke and standard stroke information of the target font, to replace the to-be-converted Chinese character.

Aspects of the present disclosure relate to processing irregularly arranged characters. An image is received. An irregularly arranged character within the image is detected. A direction of the irregularly arranged character is modified to a proper direction to obtain a properly oriented character. The properly oriented character is recognized to obtain a first identified character. The image is then rebuilt by replacing the irregularly arranged character with the first identified character, the first identified character in a machine-encoded format.

An intelligent building block-based Chinese character learning system, relating to the technical field of intelligent building block-based Chinese character learning. Chinese characters are combined freely and conveniently. Moreover, Chinese characters combined by a child are combined with online courses, so that online courses are triggered by means of assembly by the child, and interactive Chinese character teaching is implemented. The system comprises: an instruction module, used for bearing Chinese characters and building block assembly of the Chinese characters; a master control board module, used for reading the Chinese characters assembled by the instruction modules, displaying the assembled Chinese characters, and synchronizing the assembled Chinese characters to a mobile phone or an iPad; and an online course module, used for reading the Chinese characters synchronized by the master control board module and searching for an online course.

According to implementations of the subject matter described herein, there is provided a solution for text recognition in an image. In this solution, a target text line area, which is expected to include a text to be recognized, is determined from an image. Probability distribution information of a character model element(s) present in the target text line area is determined using a single character model. The single character model is trained based on training text line areas and respective ground-truth texts in the training text line areas. Texts in the training text line areas are arranged in different orientations, and/or the ground-truth texts comprise texts are related to various languages (e.g., texts related to a Latin and an Eastern languages). The text in the target text line area can be determined based on the determined probability distribution information. The single character model enables more efficient and convenient text recognition.

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.

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.