The Recognition of Finger-Spelling for Chinese Sign Language

摘要

In this paper 3-layer feedforward network is introduced to recognize Chinese manual alphabet, and Single Parameter Dynamic Search Algorithm(SPDS) is used to learn net parameters. In addition, a recognition algorithm for recognizing manual alphabets based on multi-features and multi-classifiers is proposed to promote the recognition performance of finger-spelling. From experiment result, it is shown that Chinese finger-spelling recognition based on multi-features and multi-classifiers outperforms its recognition based on single-classifier.