Arabic Sign Language Recognition Using Pulse-Coupled Neural Network and Discrete Fourier Transforms

M. Saied Abdel-Wahab , Moustafa Syam2, Magdy Aboul-Ela3, and Ahmed Samir4
,2,4 Faculty of Computers and Information, Ain-Shams University; Cairo, Egypt.
3 Sadat Academy for Management Sciences; Maadi, Cairo, Egypt.

Abstract:
Hand gestures play an important role in communication between people during their daily lives. However, the extensive use of hand gestures as a mean of communication can be found in sign languages. Sign language is the basic communication method between deaf people. A translator is usually needed when an ordinary person wants to communicate with a deaf one. The work presented in this paper aims to developing a system for automatic translation of gestures of the manual alphabets in the Arabic sign language. The proposed system uses Discrete Fourier Transforms on the global pulse signal of a pulse-coupled neural network (PCNN). We describe the mathematical model of the (PCNN) and an original way of analyzing the pulse of the network in order to achieve scale and translation-independent recognition for isolated gestures.

Keywords: Gesture, Posture, PCNN, Discrete Fourier Transform (DFT), Multi-
                   layer perceptron (MLP).