Signsage: Sign Language to Text Translator website for Inclusive Education

Signsage is a Python based program designed to minimize the communication barrier between the deaf community and the hearing impaired in educational classes. It uses sign language recognition in real-time to provide text translations for the hand gestures used offering a natural interface for the students and the teachers.

for code click here

Key Features:

• Real-time Sign Language Recognition: Uses computer vision to decode hand movements from videos that are captured from a webcam.
• Text Translation: Translates accepted signals into text that is readable through the internet, usually in graphical user interface form.
• Inclusive Communication: Also helps the teachers to be able to communicate easily with the deaf students making the learning environment even more effective.
• Customizable Sign Language Set: It enables instructors to customize only specific signals within the system, which are relevant to the learning standards and ultimately improves comprehensiveness.

Requirements

• Python 3.x
• OpenCV (cv2)
• NumPy
• Mediapipe
• Scikit-learn
• Flask

Words available for interpretation

Usage

1. Clone the repository.
2. Make sure you have Python 3.x installed on your system along with the required libraries.

  python -m pip install -r requirements.txt

3. Go into the root folder of the project and run the app by using the following command.

   python app.py

4. Go to the development server at: http://localhost:5000

Navigating through the project

scripts/one_collect_imgs.py

This script allows you to collect real-time image data from your webcam with specified labels. It creates a dataset for each label by capturing images and storing them in separate directories within a specified data directory.

Usage

1. Run the script.
2. Enter the labels you want to create when prompted. Enter -1 to stop adding labels.
3. Once labels are entered, the webcam will activate.
4. Press Q to start capturing images for each label.
5. Images will be stored in the specified data directory under separate folders for each label.

Parameters

• DATA_DIR: Directory to store the collected data. Default is ./data.
• dataset_size: Number of images to collect for each label. Default is 300.

Notes

• Ensure proper lighting and background for accurate image collection.
• Press Q to start capturing images after each label prompt.

scripts/two_create_dataset.py

This script captures images from a specified directory, detects hand landmarks using the MediaPipe library, and saves the landmark data along with corresponding labels into a pickle file.

Usage

1. Place your image data in the specified data directory (./data by default).
2. Run the script.
3. The script will process each image, extract hand landmarks, and save the data along with labels into a pickle file named data.pickle.

Parameters

• DATA_DIR: Directory containing the image data. Default is ./data.

Notes

• Ensure your images have sufficient resolution and quality for accurate hand landmark detection.
• The script assumes that each subdirectory in the data directory represents a different label/class.
• Hand landmark data is saved as a list of coordinates relative to the top-left corner of the bounding box of the detected hand.
• The pickle file data.pickle contains a dictionary with keys 'data' and 'labels', where 'data' is a list of hand landmark data and 'labels' is a list of corresponding labels.

scripts/three_train_classifier.py

This script trains a Random Forest classifier for gesture recognition using hand landmarks data. It also evaluates the model’s performance using cross-validation and saves the trained model for future use.

Usage

1. Ensure you have hand landmarks data saved as data.pickle in the project directory.
2. Run the script.
3. The script will load the hand landmarks data, preprocess it, train a Random Forest classifier, and evaluate its performance.

Notes

• Hand landmarks data should be saved as a dictionary (labels_dict.py)containing 'data' (list of hand landmark data) and 'labels' (list of corresponding labels).
• The script pads each hand landmark sequence with zeros to ensure all sequences have the same length, necessary for training the classifier.
• The classifier is trained using stratified train-test split and evaluated using cross-validation for robustness.
• The trained model is saved as model.p using the pickle module for future use.
• Adjust the model parameters and preprocessing steps as needed for improved performance.

scripts/four_inference_classifier.py

This script performs real-time gesture recognition using hand landmarks detected by the MediaPipe library. It loads a pre-trained gesture classification model and overlays the predicted gesture label on the input video stream.

Usage

1. Ensure you have a trained gesture classification model saved as model.p in the project directory.
2. Run the script.
3. The script will activate your webcam and overlay the predicted gesture label on the detected hand landmarks in real-time.

Notes

• The gesture classification model is assumed to be trained externally and saved using the pickle module.
• Hand landmarks are detected using the MediaPipe library, providing a robust representation of hand gestures.
• The script draws bounding boxes around detected hands and overlays the predicted gesture label on the video stream.
• Adjust the min_detection_confidence parameter of the Hands class for controlling the confidence threshold of hand landmark detection.
• Ensure proper lighting and background for accurate hand landmark detection and gesture recognition.

app.py

This Flask-based web application streams real-time video from your webcam and performs gesture recognition using a pre-trained model. The predicted gesture labels are overlaid on the video stream and displayed on a web page.

Usage

1. Ensure you have your pre-trained gesture classification model saved and inference code ready.
2. Run the Flask application (app.py).
3. Open your web browser and navigate to http://localhost:5000 or http://127.0.0.1:5000.
4. You should see the real-time video stream with predicted gesture labels overlaid.

Notes

• The GestureClassifier class is assumed to be implemented in inference_classifier.py.
• The Flask application captures frames from the webcam using OpenCV, performs gesture recognition using the GestureClassifier class, and streams the processed frames to the web page.
• Ensure proper permissions for accessing the webcam.
• Adjust the URL (http://localhost:5000) according to your Flask application settings.