Abstract
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 multifeatures 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 multiclassifiers outperforms its recognition based on single-classifier.
Cite
CITATION STYLE
Jiangqin, W., & Wen, G. (2002). The recognition of finger-spelling for Chinese sign language. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2298, pp. 96–100). Springer Verlag. https://doi.org/10.1007/3-540-47873-6_10
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.
