Sign Language with Vision
Computer vision system that detects and translates sign language gestures in real time. Uses MediaPipe for hand tracking, OpenCV for video processing and a trained neural network to classify each sign. Built with Python, TensorFlow and Keras.
Real-Time AI
Computer Vision & Deep Learning
Key Features
Key features of the recognition system
Real-Time Hand Detection
MediaPipe tracks up to 21 landmarks per hand in the camera feed with high accuracy and performance.
Neural Network Classification
Deep learning model trained with TensorFlow/Keras to recognize gesture patterns and classify each sign.
Custom Dataset
Manual collection and labeling of samples for each letter or gesture to build a balanced, representative dataset.
Letter and Word Translation
Recognizes the fingerspelling alphabet (letters) and common sign language words, showing translated text.
Streaming Processing
Optimized processing of the video stream for near-instant response without sacrificing accuracy.
Visual Interface
Display window that draws the hand skeleton, the detected letter and the sign history on screen.
Tech Stack
Computer vision and machine learning tools
Challenges and Solutions
Technical problems solved during development
Robust Hand Detection
Achieving stable hand tracking across different lighting conditions, backgrounds and skin tones.
Dataset and Imbalanced Classes
Collecting enough samples of each letter and preventing the model from biasing toward classes with more examples.
Real-Time Latency
Classifying every video frame without the translation feeling slow or choppy.
Model Generalization
Making the model work well with hands of different users, sizes and angles, not just training data.
Source Code
Explore the project's full code on GitHub. Includes the training notebook, data capture scripts, dataset and real-time detector.
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