Feature dimension-reduction optimization method for Chinese character recognition

Abstract

The invention discloses a feature dimension-reduction optimization method for Chinese character recognition. The feature dimension-reduction optimization method comprises a first step of conducting preprocessing and feature extraction on a Chinese character sample and conducting LDA dimension reduction changing on the extracted Chinese character features, a second step of conducting classification and recognition by means of a minimum Euclidean distance classifier, a third step of regarding the sample with wrong classification and recognition as a newly-added sample, adding the newly-added sample to an original sample set, and conducting the dimension reduction changing again by means of a learning method of ILDA incremental linear judgment, a fourth step of conducting the classification and recognition again by means of the minimum Euclidean distance classifier, and a fifth step of repeating the third step and the fourth step, outputting the LDA optimizing parameter after repeated iterative calculation, and enabling the LDA optimized parameter to be used for the classification and recognition of Chinese characters. The feature dimension-reduction optimization method for the Chinese character recognition overcomes the defect that an existing LDA changing method can not effectively optimize the LDA transformation matrix parameters by means of recognition classification and information, and has the advantages that the performance of the LDA feature dimension reduction changing and the accuracy rate of the character recognition can be greatly improved.

Description

technical field [0001] The invention relates to a pattern recognition and artificial intelligence technology, in particular to a feature dimensionality reduction optimization method for Chinese character recognition. Background technique [0002] A typical Chinese character recognition system is generally divided into three modules: feature extraction of Chinese character samples, classification recognition, and post-processing. The feature extraction module is one of the key steps in the Chinese character recognition system and has an important impact on the performance of the entire system. Feature dimensionality reduction is one of the commonly used techniques in feature extraction. It can not only reduce the storage space of features, but also effectively improve the recognition rate of Chinese characters. The feature dimension reduction method based on LDA is the main feature dimension reduction method in Chinese character recognition at present. Aiming at the deficien...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to overcome the shortcomings and deficiencies of the prior art, and provide a feature dimensionality reduction optimization method for Chinese character recognition. and incremental linear decision learning method, a Chinese character feature dimensionality reduction optimization method based on Boosting technology is proposed

Images