Created on 12th March 2025
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Communication barriers between the deaf community and non-sign language users make everyday interactions difficult. This project bridges that gap by using computer vision and machine learning to translate sign language into text in real time. It enhances accessibility and inclusivity in workplaces, education, and daily communication.
Gesture Recognition Accuracy: Training the model to recognize different hand signs with high accuracy was challenging. We improved performance by using a diverse dataset and fine-tuning the model.
Real-Time Processing: Ensuring low latency in translation required optimization. We leveraged efficient deep learning models and hardware acceleration.
Variability in Lighting and Backgrounds: The system initially struggled with different environments. Implementing adaptive preprocessing techniques helped stabilize detection.
Tracks Applied (1)
Technologies used