Mexican Sign Language Recognition Using Jacobi-Fourier Moments


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.

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Solís, F. , Toxqui, C. and Martínez, D. (2015) Mexican Sign Language Recognition Using Jacobi-Fourier Moments. Engineering, 7, 700-705. doi: 10.4236/eng.2015.710061.

Conflicts of Interest

The authors declare no conflicts of interest.


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