Collaborative stroke optimization-based personalized handwriting migration method and system

Abstract

The invention provides a collaborative stroke optimization-based personalized handwriting migration method and system, and the method comprises the steps of carrying out the deformation processing ofthe size and the proportion on a to-be-input Chinese character according to the average area proportion and the average length-width ratio of a target Chinese character so as to enable the to-be-inputChinese character to be aligned with the skeleton of a target Chinese character; inputting the Chinese characters subjected to deformation processing into a target neural network, and outputting thecorresponding target Chinese characters through the target neural network, wherein the target neural network refers to a trained confrontation generation network and is used for converting inputted Chinese characters into the target font Chinese characters, so that the purpose that any printed Chinese character is migrated into another printed Chinese character or a handwritten Chinese character by means of a small number of data sets is achieved, even the personalized handwritten font customization can be achieved, and a target font can be generated rapidly, accurately and realistically.

Description

technical field [0001] The invention relates to the technical fields of computer vision and image processing, in particular to a personalized handwriting migration method and system based on collaborative stroke optimization. Background technique [0002] Fonts are an important part of media content creativity, and are widely used in various visual communication designs in contemporary society, meeting the multi-directional and multi-level needs of mass media. The development of a set of Chinese fonts requires a lot of manpower and time. Different from the English alphabet (including uppercase and lowercase) that only contains 52 characters, at present, the lowest standard Chinese character encoding character set - GB2312-80 national standard code has selected a total of 6763 Chinese characters (including 3755 commonly used Chinese characters at the first level, and 3755 common Chinese characters at the second level. Second commonly used Chinese characters 3008). Therefore...

AI Technical Summary

Problems solved by technology

[0004] However, the existing methods mainly have the following two limitations: none of the existing font generation models based on image conversion considers the introduction of a process or module for optimizing Chinese character strokes in the model; most existing font generation methods rely on Learning from a large number of paired training samples (3000 pairs) does not greatly reduce time and labor costs, and is not very practical

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