SignBridge AI: A Multimodal Deep Learning Framework for Real Time Sign Language Translation

作者：Rajeshri U. Vaidya · 发表于：International Journal for Research in Applied Science and Engineering Technology · 年份：2026 · DOI：10.22214/ijraset.2026.79000

Sign language recognition systems play an important role in improving communication between deaf or hard-ofhearing individuals and the hearing population. However, the lack of widespread understanding of sign language continues to create barriers in education, healthcare, and everyday social interaction. This paper presents a real-time vision-based sign language recognition system using Convolutional Neural Networks (CNNs) and computer vision techniques to translate hand gestures into text and speech. The proposed system captures gesture images through a webcam and processes them using OpenCV-based hand segmentation and preprocessing methods. A custom dataset consisting of 44 gesture classes, including alphabets and numerical signs, was created using grayscale images of size 50×50 pixels. Data augmentation techniques such as image flipping were applied to improve model performance and generalization. The processed gesture images were used to train a CNN model implemented using TensorFlow and Keras for accurate classification of hand gestures. The trained model enables real-time gesture prediction through live video input and converts recognized gestures into readable text and speech output using a text-to-speech module. Experimental results demonstrate that the proposed system provides efficient and accurate gesture recognition suitable for real-time applications. This work contributes to the development of an accessible, low-cost assistive communication system for sign langu...