TITLE:
Mexican Sign Language Recognition Using Jacobi-Fourier Moments
AUTHORS:
Francisco Solís, Carina Toxqui, David Martínez
KEYWORDS:
Mexican Sign Language, Jacobi-Fourier Moments, Digital Image Processing
JOURNAL NAME:
Engineering,
Vol.7 No.10,
October
30,
2015
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.