Handwritten sample recognition method and system based on sample enhancement

Abstract

The invention discloses a handwritten sample recognition method and system based on sample enhancement, and the method comprises the steps: S1, generating a labeled sample, marking handwritten characters in an image sample, cutting the handwritten characters out of the image sample, and carrying out the classification of the cut handwritten characters; S2, sample enhancement: carrying out random transformation on the labeled samples to generate transformed samples, and generating enhanced samples in the same distribution as the transformed samples by utilizing a generation model; S3, sample synthesis: generating a training sample by using the enhanced sample; S4, model training: a training sample is used to train a detection classification model and a handwritten sample identification model; and S5, identification application: detecting the position of the handwritten character by using the trained detection classification model, and then identifying the handwritten character by usingthe handwritten sample identification model. The detection classification model and the recognition model are optimized by increasing the diversity of the training samples, and the problems that in the prior art, the offline handwriting recognition accuracy is low, the handwriting recognition samples are difficult to mark, and the model is lifted slowly are effectively solved.

Description

technical field [0001] The invention relates to the technical field of text image intelligent recognition, in particular to a method and system for handwritten sample recognition based on sample enhancement. Background technique [0002] Text images mainly refer to documents that convert paper documents into image formats in some way. Text images may include: tables, pictures, machine-generated fonts, and even handwritten handwriting. Handwritten handwriting includes handwritten fonts and handwritten graphics. Generally speaking, optical character recognition and deep neural network recognition are usually used for image recognition of non-handwritten handwriting. For handwriting recognition, online recognition and offline recognition are usually used. The online recognition mainly refers to obtaining information such as stroke order and pressure sensitivity of handwritten handwriting, judging the handwriting content through a model, and feeding back all possible results to...

AI Technical Summary

Problems solved by technology

However, in the existing offline recognition, due to the lack of uniform standards for handwritten handwriting compared with general fonts, the handwriting styles of writers are quite different, and handwriting is scribbled and continuous strokes are common, resulting in low recognition rate and difficulty in labeling handwriting recognition samples. slow up

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