Mexican sign language recognition using normalized moments and artificial neural networks
J-Francisco Solís-V., Carina Toxqui‐Quitl, David Martínez-Martínez, Margarita H.-G. · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2014
This work presents a framework designed for the Mexican Sign Language (MSL) recognition. A data set was recorded with 24 static signs from the MSL using 5 different versions, this MSL dataset was captured using a digital camera in incoherent light conditions. Digital Image Processing was used to segment hand gestures, a uniform background was selected to avoid using gloved hands or some special markers. Feature extraction was performed by calculating normalized geometric moments of gray scaled signs, then an Artificial Neural Network performs the recognition using a 10-fold cross validation tested in weka, the best result achieved 95.83% of recognition rate.