Machine learning based sign language recognition system that detects hand gestures and converts them into text and speech.
Real-time American Sign Language (ASL) recognition system using PyTorch and MediaPipe. Recognizes 25 common ASL gestures with 76.05% accuracy, optimized for RTX4070 GPUs. Features live webcam recognition, hybrid TCN+LSTM+Transformer architecture, and comprehensive training pipeline.
Real-time American Sign Language (ASL) detection system using Deep Learning (29 classes). Built with TensorFlow/Keras and OpenCV.
Real-time Sign Language Recognition system using a CNN model to translate 36 hand gestures into text with 95%+ accuracy.
Real-time ASL recognition system using MediaPipe hand tracking and neural networks. Achieves 92% accuracy with 99% reduced input dimensionality through skeletal coordinate analysis.
Real-time American Sign Language recognition using ResNet50 + Vision Transformer with 99.93% accuracy.
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