A method of training a cycle generative networks model and a method of building a character library are provided, which relate to a field of artificial intelligence, in particular to a computer vision and deep learning technology, and which may be applied to a scene such as image processing and image recognition. A specific implementation scheme includes: inputting a source domain sample character into the cycle generative networks model to obtain a first target domain generated character; calculating a character error loss and a feature loss of the cycle generative networks model by inputting the first target domain generated character and a preset target domain sample character into a character classification model; and adjusting a parameter of the cycle generative networks model according to the character error loss and the feature loss. An electronic device and a storage medium are further provided.

Provided is a training method for a character generation model, and a character generation method, apparatus and device, which relates to the technical field of artificial intelligences, particularly, the technical field of computer vision and deep learning. The specific implementation schemes are: a source domain sample word and a target domain style word are input into the character generation model to obtain a target domain generation word; the target domain generation word and a target domain sample word are input into a pre-trained character classification model to calculate a feature loss of the character generation model; and a parameter of the character generation model is adjusted according to the feature loss.

An information processing apparatus includes a processor configured to obtain, for each character of plural characters recognized from an image, (a) position of the character in the image, (b) size of the character, and (c) confidence level of a character recognition result of the character; and determine whether to regard the character as a noise based on a distance between the character and its nearest character, the size of the character, and the confidence level of the character recognition result of the character.

An information processing apparatus includes a processor configured to acquire (i) an image including characters and (ii) a character-recognition result obtained by applying character recognition on the image, and display, to a viewer of the character-recognition result, each character in the image and a recognized character corresponding to the character in a uniform size and at positions adjusted to indicate correspondence between the character and the recognized character.

An information processing apparatus (10) registers a set of item names corresponding to an item name field associated with an item value field in a predetermined business form and identifies an association of an item name field with an item value field included in the business form to be processed based on the registered set of item names.

A method for training a font generation model is described below. A source domain sample character and a target domain association character are input into an encoder of the font generation model to obtain a sample character content feature and an association character style feature. The sample character content feature and the association character style feature are input into an attention mechanism network to obtain a target domain style feature. The sample character content feature and the target domain style feature are input into a decoder to obtain a target domain generation character. The target domain generation character and at least one of a target domain sample character or the target domain association character are input into a loss analysis network of the font generation model to obtain a model loss, and a parameter of the font generation model is adjusted according to the model loss.

According to one embodiment, a dictionary editing apparatus includes processing circuitry. The processing circuitry is configured to extract words from text data, append character pronunciations to the extracted words, and specify, when a modification is made to word information including the extracted words and the appended character pronunciations, a modification candidate that is a word or character pronunciation to be modified in relation to the modification.

Ideogram character analysis includes partitioning an original ideogram character into strokes, and mapping each stroke to a corresponding stroke identifier (id) to create an original stroke id sequence that includes stroke identifiers. A candidate ideogram character that has a candidate stroke id sequence within a threshold distance to the original stroke id sequence is selected. One or more embodiments may create a new phrase by replacing the original ideogram character with the candidate ideogram character in a search phrase. One or more embodiments perform a search using the search phrase and the new phrase to obtain a result, and present the result. One or more embodiments may replace an original ideogram character in a character recognized document with the candidate ideogram character and store the character recognized document.

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.

Provided is a ledger recognition system which can enhance recognition accuracy of a handwritten character filled out by a user thus capable of reducing a manual work in a correction operation. A ledger recognition system includes: a headquarter server configured to recognize handwritten characters described in a ledger by a user; a system terminal including an image scanner for reading the handwritten characters filled out in the ledger by the user; and a public telecommunication network which allows the headquarter server and the system terminal to be communicably connected with each other. The headquarter server includes a handwritten character recognition unit where the handwritten character recognition unit receives the image data of the ledger read by the image scanner from the system terminal, recognizes the handwritten characters written by the user in the image data of the received ledger in accordance with at least two types of OCR recognition programs having different algorithms, determines the handwritten characters described in the ledger with respect to a part of the handwritten characters where recognition results in accordance with the OCR recognition programs agree with each other, and sets a part of the handwritten characters where the recognition results by the OCR recognition programs do not agree with each other as an object of correction processing.