The present disclosure relates to a computer-implemented method for handwriting-to-text-summarization, comprising obtaining, via a user interface of a system, a handwriting input representing a handwriting of a user of the system for handwriting-to-text-summarization, recognizing a text in the handwriting input, extracting at least one dynamic feature of the handwriting from the handwriting input, generating a text summary of the text, wherein generating the text summary is based on the text and on the at least one dynamic feature of the handwriting. The present disclosure also relates to a system for handwriting-to-text-summarization, comprising a user interface comprising a capturing subsystem configured to capture a handwriting of a user of the system, and wherein the system is configured to run the method for handwriting-to-text-summarization.

The present disclosure discloses a method for training an adversarial network model, a method for building a character library, an electronic device and a storage medium, which relate to a field of artificial intelligence, in particular to a field of computer vision and deep learning technologies, and are applicable in a scene of image processing and image recognition. The method for training includes: generating a new character by using the generation model based on a stroke character sample and a line character sample; discriminating a reality of the generated new character by using the discrimination model; calculating a basic loss based on the new character and a discrimination result; calculating a track consistency loss based on a track consistency between the line character sample and the new character; and adjusting a parameter of the generation model according to the basic loss and the track consistency loss.

A method of generating a font database, and a method of training a neural network model are provided, which relate to a field of artificial intelligence, in particular to a computer vision and deep learning technology. The method of generating the font database includes: determining, by using a trained similarity comparison model, a basic font database most similar to handwriting font data of a target user in a plurality of basic font databases as a candidate font database; and adjusting, by using a trained basic font database model for generating the candidate font database, the handwriting font data of the target user, so as to obtain a target font database for the target user.

A method for generating word code, a method and device for recognizing codes are provided, and they are falling within the field of machine visual recognition. The method for recognizing word code includes: acquiring an image containing word codes, and recognizing target words therein; splitting the target words according to the same rule, and then recognizing all visual anomaly split sequences, and performing determination on the word codes, if the word codes are generated on the basis of an encoding mechanism designed by a system, directly reading same and performing invoking; otherwise, invoking a pre-inputted target file of the word codes according to target word meaning sequence numbers and visual anomaly sequences numbers corresponding to the word codes.

A method of generating a font database, and a method of training a neural network model are provided, which relate to a field of artificial intelligence, in particular to a computer vision and deep learning technology. The method of generating the font database includes: determining, by using a trained similarity comparison model, a basic font database most similar to handwriting font data of a target user in a plurality of basic font databases as a candidate font database; and adjusting, by using a trained basic font database model for generating the candidate font database, the handwriting font data of the target user, so as to obtain a target font database for the target user.

The present disclosure relates to a handwritten text recognition method, including: acquiring an information sequence including a plurality of track points of handwritten text, wherein information on each track point comprises its abscissa, writing time and writing state value; dividing the plurality of track points into a plurality of strokes according to the writing state value of each track point, the writing state value including a first value representative of stroke pen-up and a second value representative of stroke pen-down, respectively; calculating a first segmentation threshold of the handwritten text; determining a first text segmentation point according to a result of comparison between an absolute value of a difference between abscissas of a start track point of one stroke and an end track point of its previous stroke and the first segmentation threshold; and performing text segmentation according to the first text segmentation point to obtain a text segmentation result.

A display apparatus includes circuitry that displays a table on a screen. The circuitry acquires, from a memory, an attribute associated with the table; receives handwritten data input to the table by a user operation; and converts the handwritten data into a character string so as to have an appearance in accordance with the attribute associated with the table.

Disclosed is an electronic device including: a memory, and a processor operatively connected to the memory. The memory stores instructions which, when executed, cause the processor to: obtain handwriting data including at least one letter; align the at least one letter with a reference line to generate target handwriting data; change at least one of a position or an angle of the at least one letter to generate distorted handwriting data; obtain correction information for correcting the distorted handwriting data to correspond to the target handwriting data; and store the correction information in the memory.

A method for generating word code, a method and device for recognizing codes are provided, and they are falling within the field of machine visual recognition. The method for recognizing word code includes: acquiring an image containing word codes, and recognizing target words therein; splitting the target words according to the same rule, and then recognizing all visual anomaly split sequences, and performing determination on the word codes, if the word codes are generated on the basis of an encoding mechanism designed by a system, directly reading same and performing invoking; otherwise, invoking a pre-inputted target file of the word codes according to target word meaning sequence numbers and visual anomaly sequences numbers corresponding to the word codes.

A calligraphy-painting device, a calligraphy-painting apparatus, and an auxiliary method for calligraphy-painting are disclosed. The calligraphy-painting device includes: a display portion, configured to display a preset calligraphy-painting information; an image acquiring portion, configured to acquire an image; and a control unit, in communication connection with the display portion, and configured to control the display portion to display the preset calligraphy-painting information, wherein the image is processed to obtain a calligraphy-painting region, and the preset calligraphy-painting information is virtually displayed in the calligraphy-painting region.