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Mexican Sign Language Recognition Using Jacobi-Fourier Moments
University Center UAEM Teotihucan Valley, Autonomous University of Mexico State, México
Polytechnic University of Tulancingo, Tulancingo, México
University Center UAEM Teotihucan Valley, Autonomous University of Mexico State, México
- 1 University Center UAEM Teotihucan Valley, Autonomous University of Mexico State, México
- 2 Polytechnic University of Tulancingo, Tulancingo, México
- 3 University Center UAEM Teotihucan Valley, Autonomous University of Mexico State, México
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Abstract
The present work introduces a system for recognizing static signs in Mexican Sign Language (MSL) using Jacobi-Fourier Moments (JFMs) and Artificial Neural Networks (ANN). The original color images of static signs are cropped, segmented and converted to grayscale. Then to reduce computational costs 64 JFMs were calculated to represent each image. The JFMs are sorted to select a subset that improves recognition according to a metric proposed by us based on a ratio between dispersion measures. Using WEKA software to test a Multilayer-Perceptron with this subset of JFMs reached 95% of recognition rate.
KeywordsMexican Sign LanguageJacobi-Fourier MomentsDigital Image Processing
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