Hand Sign Language Recognition and Conversion into Text Using Convolutional Neural Network

Hand Sign Language Recognition and Conversion into Text Using Convolutional Neural Network

Prof. Jyotsna Nanajkar

Harsh Kshirsagar, Aniket Shelke, Nikita Shinde, Manoj Thombre

Zeal College of Engineering and Research

Abstract- The current research looks into using convolutional neural networks, also known as CNNs, to develop a hand sign understanding and conversion system. The study addresses the urgent need for a precise and effective way to close the communication gap that exists between the hearing and the deaf communities. The study examines how well CNNs can identify and translate hand sign gestures into text, giving sign language users a real-time means of communication. We use three steps in our methodology: gathering datasets, training models, and thorough assessment. The outcomes show encouraging hand sign recognition accuracy, creating new opportunities for inclusive communication.

Sign language is one of the oldest and most natural forms 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 our 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. Our method provides 95.7 % accuracy for the 26 letters of the alphabet.

Keywords-Sign Language, ASL, Hearing disability, Convolutional Neural Network (CNN), Computer Vision, Machine Learning, Gesture recognition, Sign language recognition, Hue Saturation Value algorithm.