🤟 ASL Recognizer: A real-time American Sign Language translation system utilizing MediaPipe for hand-landmark extraction and Deep Learning for gesture classification.
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Updated
Nov 7, 2025 - Jupyter Notebook
🤟 ASL Recognizer: A real-time American Sign Language translation system utilizing MediaPipe for hand-landmark extraction and Deep Learning for gesture classification.
Real-time Sign Language Recognition system using a CNN model to translate 36 hand gestures into text with 95%+ accuracy.
Machine learning based sign language recognition system that detects hand gestures and converts them into text and speech.
Real-time Hand Sign Language Detection System using Teachable Machine, TensorFlow, OpenCV, and CVZone. Recognizes ASL alphabet gestures (A–Z) through webcam-based hand tracking and deep learning.
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 recognition using ResNet50 + Vision Transformer with 99.93% accuracy.
Modular Computer Vision project for hand gesture recognition, sign language interpretation, and air-writing with integrated text-to-speech and automated emergency email alerts.
Detecting and recognizing sign language gestures from short videos using hybrid deep learning models.
Real-time American Sign Language gesture recognition system using MediaPipe hand landmarks and Random Forest classification.
AI-powered American Sign Language (ASL) recognition system that translates hand gestures into readable English text in real-time.
TensorFlow CNN for American Sign Language alphabet image classification with data augmentation, validation split and model export.
Real-time American Sign Language (ASL) detection system using Deep Learning (29 classes). Built with TensorFlow/Keras and OpenCV.
SpeakSign AI is a real-time Voice-to-Sign Language Translator & ASL Gesture Recognition Web Studio built with Python, Flask, TensorFlow (CNN), and Web Speech API. 🤟 Real-time speech-to-sign conversion & live webcam gesture classification.
Real-Time Sub-10ms ASL Gesture Recognition Pipeline using Google MediaPipe 3D Hand Landmarks and LightGBM GBDT
Real-time sign language recognition with MediaPipe hand/face/pose landmarks and a two-stream (appearance + motion) 1D-CNN + Transformer with motion-gated attention. Trained on 36-class ASL static gestures (val 93.6% / test 89.6%), live Streamlit webcam inference with confidence thresholding, top-3 predictions, and validation sampling.
Mobile-first ASL fingerspelling trainer powered by MediaPipe. Earn a shareable certificate. Camera stays in your browser.
Real-time ASL Recognition Web App using MediaPipe & Machine Learning (RF, SVM, KNN). Features a unique Dual-Hand HCI mechanism for touchless typing.
Real-time American Sign Language interpreter leveraging MediaPipe, a Hand-Motion Transformer (89.56% Top-5 over 2,731 classes), FastAPI, and Angular.
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