Based on HMM and SVM Multilayer Architecture Classifier for Chinese Sign Language Recognition with Large Vocabulary

Abstract

This paper has put forward a new architecture classifier method for Chinese sign language recognition (CSLR) to improve the performance of recognition. It is a signer-independent method, to recognize Chinese sign language with large vocabulary using multilayer architecture classifier and making use of the advantages both of HMM (hidden Markov model) and SVM (support vector machines). Because HMM is good at dealing with sequential inputs, while SVM shows superior performance in classifying with good generalization properties especially for limited samples. Therefore, they can be combined to yield a better and effective multilayer architecture classifier. We apply SVMs to resolve the uncertainties of the remaining which are in confusable sets after the first-stage HMM-based recognizer. And the confusable sets would be updated dynamically according to the results of a recognition performance to optimize the discernment performance next time. Experimental results proved that it is an effective method for CSLR with large vocabulary keywords sign language recognition, HMM, SVM, multilayer architecture classifier.