  In this paper, it is introduced a hand gesture recognition system to
recognize the characters in the real time. The system consists of three
modules: real time hand tracking, training gesture and gesture recognition
using Convolutional Neural Networks. Camshift algorithm and hand blobs analysis
for hand tracking are being used to obtain motion descriptors and hand region.
It is fairy robust to background cluster and uses skin color for hand gesture
tracking and recognition. Furthermore, the techniques have been proposed to
improve the performance of the recognition and the accuracy using the
approaches like selection of the training images and the adaptive threshold
gesture to remove non-gesture pattern that helps to qualify an input pattern as
a gesture. In the experiments, it has been tested to the vocabulary of 36
gestures including the alphabets and digits, and results effectiveness of the
approach.
