Method and system for extracting and identifying handwriting stroke features

Abstract

The invention discloses a method for extracting and identifying handwriting stroke features. By means of introduction of low-rank matrix recovery and sparse projection, a handwriting image is divided into low-rank main component stroke features, remarkable stroke features and stroke errors; encoding of the main component features, extraction of the remarkable stroke features and automatic stroke error correction are achieved through a convex optimization technology, and similarity of the remarkable stroke features is kept. Obtained sparse projection shadows can be used for extracting the remarkable stroke features of handwriting training samples, and can also be used for embedding operation of test samples and extraction of identification features so as to generate a training set and a test set, the remarkable stroke features are input into a nearest neighbor classifier to obtain class information of the test samples according to similarity between the test samples and the training samples and the class of the training samples, and the most accurate handwriting identifying result is obtained. Due to the fact that low-rank and spare encoding is introduced, the main component stroke features and the remarkable stroke features with identification performance are obtained, wrong strokes can be detected, and the handwriting description and identification capacity is effectively improved.

Description

[0001] This application claims the priority of a Chinese patent application whose application date is November 28, 2014, the application number is 201410709992.5, and the invention title is "a method and system for handwritten stroke feature extraction and recognition", the entire content of which is incorporated herein by reference Applying. Technical field [0002] The invention relates to the technical field of computer vision and image recognition, and more specifically, to a method and system for extracting and recognizing handwritten stroke features. Background technique [0003] With the continuous development of computer technology and intelligence, offline handwritten character recognition has developed into a very important research topic in computer vision and pattern recognition. Offline handwriting recognition uses computers to electronicize paper images, and then analyze character images to obtain character stroke attributes. It is of great significance in the fields...

AI Technical Summary

Problems solved by technology

[0006] In view of this, the present invention provides a handwritten stroke feature extraction and recognition method and system, to overcome the complexity of calculation and the problem of not considering the maintenance of feature similarity in the prior art when new data is input

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