A Transfer Learning Based Approach For American Sign Language Recognition Using Deep Convolutional Neural Network

American Sign Language (ASL), a visual language utilizing hand gestures, facial expressions, and body movements, remains less recognized than spoken languages, resulting in communication challenges between deaf and hearing individuals. This pioneering research paper introduces an exceptionally effective method for ASL gesture recognition through image processing and computer vision. By capturing webcam images of users signing and applying advanced algorithms, the system extracts crucial features like hand position, shape, and movement to classify signs accurately. The image processing pipeline employs techniques like background subtraction, hand detection, tracking, and feature extraction, utilizing a self-prepared dataset of around 10,000 images. This holistic approach achieves an impressive average recognition accuracy of 99.2% for 26 ASL signs in real-time. This research has the potential to greatly enhance accessibility and the quality of life for the deaf and hard-of-hearing community.