Sign Language to Speech: A Simple Hackathon Project
Inspiration
Sign language is a vital means of communication for individuals with hearing and speech impairments. However, not everyone understands sign language, creating a barrier in daily interactions. We wanted to bridge this gap by developing a simple tool that translates sign language gestures into text using a webcam and computer vision.
What We Learned
- Computer Vision Basics: We explored OpenCV and MediaPipe for real-time hand tracking.
- Machine Learning Models: We learned about training a basic gesture recognition model.
- Text Processing: Converting recognized gestures into meaningful sentences.
- Hackathon Mindset: How to build a semi-functional prototype in a limited time frame.
How We Built It
- Hand Tracking with MediaPipe: We used Google’s MediaPipe Hand Tracking to detect and track hand movements.
- Gesture Classification: We trained a simple model using TensorFlow/Keras to recognize specific hand gestures corresponding to letters or words.
- Text Conversion: The recognized gestures were converted into text and displayed on the screen in real-time.
- User Interface: Using Python and OpenCV, we built a simple UI to show live video input and detected text output.
Challenges We Faced
- Data Collection: Gathering enough labeled data for accurate gesture recognition in a short time was challenging.
- Model Accuracy: Some gestures were misclassified due to lighting conditions and hand positioning variations.
- Real-time Processing: Ensuring minimal lag in recognition for a smooth user experience.
Future Improvements
- Expanding the dataset for more accurate recognition.
- Implementing speech synthesis to read out the translated text.
- Supporting dynamic sign language (gesture sequences) for complete sentence translation.
This project was an exciting dive into computer vision and accessibility solutions, and we look forward to refining it further!
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