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This document presents a computer vision system for the automatic recognition of Mexican Sign Language MSL, based on normalized moments as invariant to translation and scale transforms descriptors, using artificial neural networks as pattern recognition model. An experimental feature selection was performed to reduce computational costs due to this work focusing on automatic recognition. The computer vision system includes four LED-reflectors of 700 lumens each in order to improve image acquisition quality; this illumination system allows reducing shadows in each sign of the MSL. MSL contains 27 signs in total but 6 of them are expressed with movement; this paper presents a framework for the automatic recognition of 21 static signs of MSL. The proposed system achieved 93% of recognition rate.

KEYWORDS

Mexican Sign Language, Automatic Sign Language Recognition, Normalized Moments, Computer Vision System

Cite this paper

Solís, F. , Martínez, D. and Espinoza, O. 2016 Automatic Mexican Sign Language Recognition Using Normalized Moments and Artificial Neural Networks. Engineering, 8, 733-740. doi: 10.4236-eng.2016.810066.





Author: Francisco Solís, David Martínez, Oscar Espinoza

Source: http://www.scirp.org/



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