🤟 Real-Time ASL to Indian Regional Language Translator

Most sign language translators recognize ASL but output only in English, limiting accessibility. This project builds a real-time ASL recognition system that translates recognized gestures into text across multiple Indian regional languages, promoting inclusivity and language accessibility for diverse users. The application features a graphical user interface (GUI) for ease of use.

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✨ Features

• Real-time ASL Recognition: Detects and recognizes ASL alphabets from live webcam feed.
• Multi-language Translation: Translates recognized ASL text into several Indian regional languages (Hindi, Tamil, Telugu, Bengali, Marathi, Gujarati, Kannada, Malayalam, Punjabi) and English.
• Interactive GUI: A user-friendly interface to control the camera, view predictions, manage recognized text, and perform translations.
• Confidence and Stability Tracking: Displays prediction confidence and stability to ensure accurate recognition.
• Text Management: Allows adding recognized letters, spaces, and deleting the last character.

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🚀 Setup and Installation

Follow these steps to get the project up and running on your local machine.

📋 Prerequisites

Before you begin, ensure you have the following installed:

• Python 3.x: Download and install Python from python.org.
• pip: Python's package installer (usually comes with Python).

📦 Install Required Libraries

Open your terminal or command prompt and run the following command to install all necessary Python libraries:

pip install opencv-python numpy tensorflow scikit-learn googletrans==4.0.0-rc1 Pillow

🧠 Model Files

This project relies on pre-trained TensorFlow models and scikit-learn objects for hand detection and gesture recognition. Ensure the following files are present in your project's root directory:

• improved_asl_model.h5 (or best_model.h5, asl_model.h5)
• improved_label_encoder.pkl (or label_encoder.pkl)
• feature_scaler.pkl (optional, but recommended for improved performance)
• hand_detection.py

If any of these files are missing, the application will prompt you with an error. You would typically generate these files by running a model training script (i.e., model_trainer.py).

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▶️ How to Run the Application

To start the ASL Translator application with the graphical user interface:

1. Navigate to the project directory: Open your terminal or command prompt and change your current directory to the asl_translator folder:

   cd path/to/your/asl_translator

2. Run the script: Execute the complete_asl_app.py file:

   python complete_asl_app.py

3. Camera Index: The application might ask you for a camera index (e.g., 0 for the default webcam, 1 for an external one). Enter the appropriate number and press Enter.

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🎮 Usage and Controls

Once the application window appears, here's how to interact with it:

GUI Controls:

• Start Camera: Click this button to activate your webcam feed.
• Stop Camera: Click this button to stop the webcam feed.
• Add Letter: Adds the currently predicted stable ASL letter to the "Recognized Text" area.
• Add Space: Adds a space character to the "Recognized Text" area.
• Delete Last: Removes the last character from the "Recognized Text" area.
• Confidence Threshold (Slider): Adjusts the minimum confidence level required for a prediction to be considered stable. Higher values mean stricter recognition.
• Target Language (Dropdown): Select the language you want the recognized text to be translated into.
• Translate Text: Click this button to translate the content in the "Recognized Text" area to the selected target language.
• Clear All: Clears both the "Recognized Text" and "Translated Text" areas.
• Quit: Closes the application.

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📸 Screenshots

Here are some screenshots illustrating the application's interface and functionality:

GUI interface

Real Time Detection

Translation feature

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