Hana-Shamel/ESL

Egyptian Sign Language gesture detection using mediapipe and openCV # ASL - Arabic Sign Language Arabic Sign Language gesture detection using mediapipe and openCV ## Arabic Hand Gesture Recognition System ### Project Overview This system extends Kazuhito00's Hand Gesture Recognition by adding support for: - The complete 28-letter Arabic alphabet - 3 functional gestures (Space, Delete, Clear) - Real-time text output with Arabic script rendering ### Key Features ✋ 28 Arabic Letters: Custom-trained gesture models for all Arabic characters 🛠 Utility Gestures: - 👉 Space: Insert space between words - ❌ Delete: Remove last character - 🧹 Clear: Reset entire text 📜 Arabic Text Rendering: Proper RTL display with glyph shaping ⚡ Adjustable Sensitivity: Control detection speed via frame threshold ### Technologies Used - MediaPipe – Hand tracking - OpenCV – Camera processing & visualization - NumPy – Data handling - Model: CNN – Static gesture classification ### Getting Started *This section includes the requirements, how to run the app, training protocol and other more detailed information about the original hand gesture detection model* Please view the README of Kazuhito00's Hand Gesture Recognition Repo: github.com Additional requirements + arabic_reshaper 3.0.0 + python-bidi 0.6.6 + pillow 11.2.1 ### Training Protocol For Higher Accuracy Assume you want to increase the accuracy of a letter for index 23 in "keypoint_classifier_label.csv": 1. search (shift + f) for the line of code with "number +" in it 2. Add 20 after the "+" *index 0 to 9: add 0, index 10 to 19: add 10* 3. Run app.py 4. Press k to enter training mode 5. Press 2 (for 20 + 2 = 22) while doing the hand gesture to the camera to take a screenshot of the hand landmarks coordinates *Note: CSV uses 0-based indexing while labels start from 1* 6. Screenshot the gesture at least 20 times 7. Stop app.py 8. Open "keypoint_classification_EN" in Jupyter notebook and run all the cells Training done ✅ Kazuhito00's H …