Abstract
Sign language is one of the oldest and most natural form of language for communication, but since most people do not know sign language and interpreters are very difficult to come by we have come up with a real time method using neural networks for fingerspelling based american sign language. In this method, the hand is first passed through a filter and after the filter is applied the hand is passed through a classifier which predicts the class of the hand gestures. This method provides 98.00 % accuracy for the 26 letters of the alphabet. Key Words: Python, Machine Learning, OpenCV, Keras, NumPY, Data Training
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