SignMeUp is a beginner-friendly sign language translator project that recognizes a small set of hand signs using computer vision and machine learning. The current version focuses on a limited vocabulary such as Hello, Help, Thank You, and a few other basic gestures.
The goal of this project is to build a simple sign language recognition system that can:
- Collect hand sign images using a webcam
- Create a dataset for different sign classes
- Train a machine learning model on the collected images
- Test the trained model on new hand sign inputs
This project is designed as an early prototype and currently supports only a few words. It can be extended later with more gestures, better preprocessing, and real-time sentence generation.
| Existing Technology / Approach | Common Gap / Limitation | Sign Language Translator's Contribution |
|---|---|---|
| Pre-labelled sign-language datasets | Often require access to established datasets and may not reflect the gestures, users or environments targeted by a particular application. | Builds a self-curated hand-gesture dataset directly through webcam capture. |
| Image-based gesture classifiers | Many approaches operate on static images rather than continuous real-time input. | Implements real-time webcam-based inference for continuous gesture recognition. |
| Specialized assistive hardware | Hardware-dependent systems can increase deployment cost and accessibility barriers. | Designed around a standard webcam, avoiding the need for specialist sensing hardware. |
| Conventional computer-vision pipelines | Recognition performance can depend strongly on the quality and consistency of training data. | Creates a project-specific dataset and integrates it directly into the object-detection workflow. |
| Offline recognition systems | Recognition alone does not necessarily translate into an accessible interactive application. | Combines detection and live inference into an interactive communication-oriented system. |
The project focuses on an end-to-end real-time pipeline built around a self-curated dataset, connecting webcam capture, hand-gesture detection and live inference without requiring specialist hardware.
At the moment, the model supports a small number of words, such as:
- Hello
- Help
- Thank You
- Other basic signs added during data collection
You can expand the dataset to include more words or alphabets in future versions.
The first Python file is used to collect images through the webcam. For each sign/word, multiple images are captured and stored in separate folders. These images form the dataset for training.
The second Python file is used to preprocess the collected data, train the model, and test its performance on sign classification.
After training, the model can classify hand signs from input data and display the predicted word.
Make sure you have Python installed, along with the required libraries.
Common dependencies may include:
- Python 3.x
- OpenCV
- NumPy
- scikit-learn
- TensorFlow / Keras or PyTorch (depending on your implementation)
- Matplotlib This will train the model on the collected dataset and test it for sign prediction.
- Real-time webcam-based inference
- Self-curated hand-gesture dataset
- Object detection based recognition
- No pre-labelled dataset required
- Live prediction pipeline
Python | OpenCV | TensorFlow | Computer Vision
- Expand gesture vocabulary
- Improve robustness under different lighting conditions
- Add continuous sentence-level recognition
- Deploy as a browser-based application
Want to help make this project more accessible and capable? I’d love to collaborate on future enhancements — open an issue, submit a pull request, or connect with me on LinkedIn to work together.