SignaLearn: Real-Time Sign Language Recognition using Deep Learning and CNNs

Communication is the cornerstone of human interaction and the backbone of societal integration and development. For individuals who experience hearing and speech impairments, traditional verbal communication methods often present significant limitations, leaving them at a disadvantage in many social and professionalsituations. Sign language serves as a powerful and expressive visual language, uniquely enabling these individuals to convey complex ideas, emotions, and nuanced information through gestures and facial cues. Yet, the general public's limited knowledge and understanding of sign language perpetuate communication gaps and social exclusion. This research introduces SignaLearn, a sophisticated, real-time Sign Language Recognition (SLR) system built using Convolutional Neural Networks (CNNs) and implemented with the combined power of Python, TensorFlow, OpenCV, MediaPipe, and Matplotlib. Notably, SignaLearn avoids reliance on any web-basedtechnologies or frameworks, functioning entirely within a desktop environment to ensure broader accessibility, platform independence, and enhanced performance. SignaLearn operates by capturing real-time video input via a standard webcam and systematically processing this visual data through a meticulously constructed pipeline. This includes hand landmark detection using MediaPipe, precise region of interest (ROI) extraction, preprocessing procedures such as grayscale conversion andnormalization, and finally, classification using a deeply trained CNN. The underlying model has been trained on a comprehensive dataset of over 87,000 labeled ASL gesture images, enabling it to achieve high classification accuracy and robust performance across varied environmental conditions such as lighting changes and complex backgrounds. Furthermore, the system supports instantaneous prediction and visualization of ASL characters on-screen with minimal latency. Its modular architecture ensures that the framework can be easily scaled or extended to include new features, datasets, or hardware configurations. As part of future advancements, the system aims to support dynamic sign gestures, full-sentence translation, cross-linguistic gesture recognition, and seamless integration with Text-to-Speech (TTS) technologies, thereby converting visual gestures into spoken words.