A neuro-hierarchial multilayer network in the translation of the American sign language

A neuro hierarchial approach based on an adaptive back-propagation algorithm is proposed. In a separate preprocessing step, the input and the output vector are generated using the encoded-sequence representation. The algorithm is applied to translate the American sign language from spoken words. Experimental results indicate that this approach results in fast convergence, stable learning and a relatively small network size when compared to traditional methods.