Building a Sign Language Detection System with MediaPipe: Bridging Python and JavaScript

3 min readDec 21, 2023

Introduction: Sign language is a crucial means of communication for the Deaf and Hard of Hearing community. With advancements in technology, we can leverage tools like MediaPipe to create a sign language detection system that can interpret and translate sign language gestures into meaningful actions. In this blog post, we will guide you through the process of building a MediaPipe-based sign language detection system using Python and integrating it into a web application with JavaScript.

Prerequisites:

Python and JavaScript Knowledge: Ensure you have a basic understanding of Python and JavaScript, as we will be using both languages to build our system.
MediaPipe Installation: Install the MediaPipe library in your Python environment using the following command:

pip install mediapipe

3. Web Development Basics: Familiarize yourself with HTML, CSS, and JavaScript for creating a simple web application.

Step 1: Building the Sign Language Detection System in Python:

Using MediaPipe:

MediaPipe provides a comprehensive set of tools for hand tracking, pose estimation, and facial recognition. In this case, we will focus on hand tracking to detect sign language gestures.

import cv2
import mediapipe as mp

# Initialize MediaPipe Hands
mp_hands = mp.solutions.hands
hands = mp_hands.Hands()

# Function to process frames and detect hand landmarks
def detect_sign_language(frame):
    # Convert BGR to RGB
    rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
    
    # Process the frame
    results = hands.process(rgb_frame)
    
    if results.multi_hand_landmarks:
        for landmarks in results.multi_hand_landmarks:
            # Extract hand landmarks and perform gesture recognition
            # Add your custom logic for sign language interpretation here

    return frame

# Release resources
hands.close()

Step 2: Integrating with JavaScript:

Now, we need to create a bridge between Python and JavaScript to display the sign language detection results on a web page.

Flask as a Python Web Framework:

Use Flask to create a simple web server that will communicate with the JavaScript frontend.

from flask import Flask, render_template, Response
import cv2

app = Flask(__name__)
camera = cv2.VideoCapture(0)

# Function to generate video frames
def generate_frames():
    while True:
        success, frame = camera.read()
        if not success:
            break
        else:
            frame = detect_sign_language(frame)  # Use the function from Step 1
            ret, buffer = cv2.imencode('.jpg', frame)
            frame = buffer.tobytes()
            yield (b'--frame\r\n'
                   b'Content-Type: image/jpeg\r\n\r\n' + frame + b'\r\n')

# Route for the video feed
@app.route('/video_feed')
def video_feed():
    return Response(generate_frames(), mimetype='multipart/x-mixed-replace; boundary=frame')

# Route for the main page
@app.route('/')
def index():
    return render_template('index.html')

if __name__ == '__main__':
    app.run(debug=True)

HTML and JavaScript Frontend:

Create an HTML file (templates/index.html) for the web interface:

<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>Sign Language Detection</title>
</head>
<body>
    <img src="{{ url_for('video_feed') }}" alt="Sign Language Detection">
</body>
</html>

Conclusion:

By following these steps, you can create a Sign Language Detection System using MediaPipe in Python and integrate it into a web application using Flask and JavaScript. This project serves as a foundation that you can expand upon, adding custom gesture recognition logic and improving the user interface to make sign language more accessible to a wider audience.