So this one came from a very real problem — how do we help someone who communicates using sign language get their message across instantly, in text?

I thought, what if we could build a system that just looks at hand gestures, understands them, and outputs text in real time — kind of like a translator for the hearing-impaired. That’s what this project was about: building a Sign Language Recognition System using computer vision + deep learning.

🧩 What I Was Trying to Solve

Sign language isn’t just a static set of hand poses. It's a sequence — there’s motion, timing, and orientation involved. So just using image classification wasn’t enough. I needed something that could understand sequences of gestures.

So my idea was to use:

MediaPipe for fast, real-time hand tracking
LSTM to capture the sequence of hand movements
A model that could take raw webcam input and translate signs into text

🔧 What I Used to Build It

Python
MediaPipe Hands API for 21-point hand landmark detection
OpenCV to process real-time webcam input
TensorFlow / Keras to build the LSTM model
NumPy / Pandas for dataset creation
TensorBoard for visualizing training performance

🛠️ How I Built It

1️⃣ Data Collection

Used MediaPipe to track hand keypoints in each frame (21 landmarks per hand, each with x, y, z)
Recorded videos of different signs performed repeatedly to capture variation in speed and angle
Built a labeled dataset where each sample = a full sequence of keypoints over time

2️⃣ Preprocessing

Normalized landmark values to avoid scale/position issues
Applied smoothing to remove jitter in frame transitions
Stored final training set as .npy arrays for each class (e.g., "hello", "thanks", "yes", etc.)

3️⃣ Model Architecture

Used a stacked LSTM model with dropout to handle sequential hand landmark data
Each sequence was fed as a tensor of shape (frames, 63), since each frame had 21 keypoints × 3 coords

4️⃣ Training & Testing

Trained on 10–12 common signs with 100+ sequences each
Accuracy reached ~90%+ after tuning learning rate and batch size
Also plotted confusion matrices — most confusion was in signs that looked very similar

5️⃣ Real-Time Deployment

Built a simple OpenCV webcam interface
Detected gestures in real time and overlaid recognized text on the video feed

📈 Results

Real-time sign-to-text achieved at ~10–15 fps on a standard laptop
Accuracy: ~90% on common signs (like “hello,” “thank you,” “yes,” “no”)
Smooth experience with minimal lag using MediaPipe + lightweight LSTM

💡 What I Learned

The combination of traditional CV (MediaPipe) and DL (LSTM) works really well when you want both speed and accuracy
Small things like gesture smoothing and timing consistency made a huge difference in accuracy
For real-world use, you need a much bigger vocabulary and maybe even transformers for longer gesture phrases

🤝 Why It Matters

This kind of project can help build better tools for people who rely on gestures for communication — not just in day-to-day life but also in hospitals, public spaces, or learning environments.

And it touches the same space as behavioral AI tools — if your system understands motion and intent through vision, that’s a major part of human behavior modeling.

✉️ Let’s Collaborate

Still improving this, maybe will add more signs and switch to transformers later. If this space excites you, I’d love to connect.

📩 LinkedIn | 🔗 GitHub

✋ Sign Language to Text using AI: Real-Time Gesture Recognition with Mediapipe & LSTMs