Paper Title
Sign Language Predictor

Abstract
Since that sign language is not a common language, quite few people can comprehend it. The majority of hearing societies find it challenging to communicate with the deaf group because of this. One can anticipate that within a few decades, digital technology would have a significant impact on how people go about their daily lives and that everyone will communicate with machines either by gestures or speech recognition. If we are able to foresee such a future, we should consider the physically disabled and take action to help them. Hence, computerized recognition systems provide a novel technique to interpret deaf signals without the aid of a professional. The 26 hand gestures with in dataset correspond to the letters of the English alphabet, from A to Z. This paper took into account the Hand Gesture Recognition standard dataset from the Kaggle website. Convolutional Neural Network (CNN), the type of neural network and its pretrained Mobile Net model used in this study, improve the predictability of the alphabet in American Sign Language (ASL). Keywords - ASL Gestures, Deep Learning, CNN, Mobile Net