Abstract:

The ability to communicate will determine how successfully people navigate both their personal and professional lives. We may express ourselves thanks to it. In order to communicate, sign language users combine different hand gestures, positions, and movements with their arms, hands, and bodies. With the aid of sign languages, deaf-dumb persons can interact with hearing people. It comprises of word level signs, numbers, and human relations as well as fingerspelling, which spells out each letter in a word. Deaf and dumb persons, however, find it extremely challenging to communicate with regular people. So, it is difficult for them to interact with us until and unless others like us acquire the ability to communicate through sign language.The suggested system analyses and converts hand motions, which are sign language, into text using deep learning and the Python libraries OpenCV and Keras. We develop a sign detector that recognises some signs and can be readily expanded to recognise a huge variety of additional signs and hand gestures, such as the alphabets. This is broken down into three steps: building the dataset, using it to train a CNN, and predicting the records

Description:

Sign language, ASL, CNN, Keras, Hunspell, Tkinter, and Matplotlib