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

Training Protocol For Higher Accuracy

For letter with index 23 in "keypoint_classifier_label.csv":

1. (shift + f) "number +"
2. Add 20 next to "+" index 0 to 9: add 0, index 10 to 19: add 10
3. Run app
4. Press k to enter training mode
5. Press 2 (for 20 + 2 = 22) Note: CSV uses 0-based indexing while labels start from 1
6. Make the gesture 20+ times
7. Close app
8. Open "keypoint_classification_EN" in Jupyter notebook & run all cells
9. Training done ✅

Kazuhito00's Hand Gesture Recognition Repo: https://github.com/kinivi/hand-gesture-recognition-mediapipe