Robust Modelling of Static Hand Gestures using Deep Convolutional Network for Sign Language Translation

The efforts have been made in the field of Sign Language Recognition research from the last few decades. Sign language is basically an instrument for idea/information propagation through gestures, using hand, lip, and facial expressions. These features are in the form of sign's and every sign pattern has a very different meaning. Typical used gestures are hand gestures. These gestures corresponding the different signs are modelled for automatic detection & recognition of signs. This helps in automatic sign language translation. In this paper, a brief introduction of hand gestures and its modelling approach is covered. The main intent of the effort is to make a natural and robust system that translates sign language into meaningful information. Work is done on creating our own dataset that contains 10,500 images of static signs corresponding to 25 English alphabets ('A'-'Y'). CNN is used to classify these signs into their respective classes. Our proposed CNN model is inspired from the VGG16 base architecture, trained with over 8000 training and 500 validation images. In addition to these, 2000 test images are used to measure the performance of the proposed system. This paper also shows empirical comparisons among trained models and achieves up to 96.7% testing accuracy.