Authors - Nasir Ansari, Amit Kumar Sharma Abstract - A significant challenge in human-computer interaction is enabling effective communication for individuals with hearing impairments. This research addresses the need for accessible solutions by implementing a neural network-based system for recognizing American Sign Language (ASL) letters from images captured via a standard webcam. The primary objective is to reduce the communication barrier between the deaf and hearing communities by leveraging machine learning techniques for hand gesture classification. A Convolutional Neural Network (CNN) was utilized for both feature extraction and classification tasks. The model was trained on a publicly available Kaggle dataset comprising 87,000 images across 29 ASL alphabet classes. Experimental results demonstrate that the proposed model achieves high classification accuracy, indicating its potential applicability in real-time ASL translation systems. When we sit down to plan the next phase of the project, well add real-time gesture tracking, mix in voice and touch data, and shrink the whole model so it runs smoothly on phones and tablets.
