Published Paper Details:

In recent year, there has been rapid increase in the number of deaf-mute victims due to birth defects, accidents and oral diseases. Since deaf-mute people cannot communicate easily with normal people so they have to depend on some sort of visual communication. This system is based on a skin-color modelling technique so the skin color range is predetermined that will extract pixels(hand) from non- pixels(background). The images were fed into the model called the Convolutional Neural Network (CNN) for classification of images. Keras was used for training of images provided with proper lighting condition and a uniform background; the system acquired an average testing accuracy of 93.67%, of which 90.04% was attributed to ASL alphabet recognition and 97.52% for static word recognition, thus surpassing that of other related studies. The framework is developed by using python flask for a Web Based Application to establish the connection between deaf-mute and normal users. So that the Sign Language can be converted into Normal text or voice as output for the normal people. on the other end voice of the normal people is converted into text for the convenient of the deaf-mute people