A first character string obtainment unit according to one or more embodiments may obtain a first character string in response to an input character string that has been input. A similar character extraction unit extracts similar characters having similar shapes as characters in the first character string. A second character string generation unit generates one or more second character strings in which some or all of the characters in the first character string are replaced with similar characters extracted by the similar character extraction unit. Then, a conversion candidate output unit outputs the first character string and the second character strings as conversion candidates for the input character string.

An input apparatus includes a handwriting input unit configured to receive a handwritten input using a position of a pen or a user's finger in contact with a display; and a display unit configured to display the handwritten input received by the handwriting input unit on the display as a handwritten object. The input apparatus is configured to, in response to no occurrence of a change in the handwritten object during a first period, display one or more operation commands on the basis of the handwritten object.

In a word-including picture encoding method, a word area in a first picture is obtained, where a word area includes at least one character. The word area is filled to obtain a word-filled area, where a height or a width of the word-filled area is n times a preset size, and where n1. A second picture is obtained based on the word-filled area and the second picture and word alignment side information including the height or width of the word-filled area is encoded to obtain a first bitstream.

A system for determining correction of handwriting Chinese characters includes a Chinese character collector for collecting template Chinese characters which is inputted by handwriting. A feature classifier is connected to a pre-processor for automatically classifying features of the template Chinese characters. Text features of the template Chinese characters are acquired by a program. A tested handwriting Chinese character collector is connected to the feature classifier for collecting the handwriting Chinese characters to be tested. Exact external rectangles and mass centers of the handwriting Chinese characters to be tested are calculated, which are inputted to the feature classifier and the text features for the handwriting Chinese characters to be tested are calculated. A feature comparator is connected to the feature classifier and serves to compare the text features of a handwriting Chinese character to be tested with feature sets stored in a feature set database.

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.

Provided are a method and a device for recognizing an ancient book. The method comprises: extracting classification features of a target ancient book image based on a backbone network to obtain backbone classification features; detecting the backbone classification features and determining individual character positions and text line positions included in the target ancient book image; recognizing the individual character positions to obtain content information of individual characters, and predicting the text line positions to obtain a reading order of characters in the text line positions; and arranging, based on a ratio between the individual character positions and the text line positions, the content information of the individual characters following the reading order of the characters in the text line positions to obtain a recognition result of characters in the target ancient book image.

An input apparatus includes a handwriting input unit configured to receive a handwritten input using a position of a pen or a user's finger in contact with a display; and a display unit configured to display the handwritten input received by the handwriting input unit on the display as a handwritten object. The input apparatus is configured to, in response to no occurrence of a change in the handwritten object during a first period, display one or more operation commands on the basis of the handwritten object.

A training method includes: obtaining a synthesis text image set, a synthesis text image being obtained through synthesizing a real scenario background image and a random word, the synthesis text image being provided with a line text annotation box and a single character annotation box; training an initial algorithm network using the synthesis text image set, so as to obtain an intermediate model; processing a real scenario text image set using the intermediate model, so as to obtain a pseudo label of a real scenario text image, the real scenario text image being provided with a line text annotation box, the pseudo label being a single character annotation box; and training the intermediate model using the synthesis text image set and the real scenario text image set having the pseudo label, so as to obtain the single character detection model.

Methods and supporting systems for representing and searching characters, comprising: obtaining an image of a 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.