om192006/sign_language_keypoints

Sign Language and Gesture Recognition: MediaPipe Keypoints

Dataset Summary

This dataset contains pre-extracted MediaPipe keypoints for 29 distinct sign language gestures. It is specifically designed to train lightweight machine learning models—such as Dense Neural Networks (DNNs) or LSTMs—for real-time sign language translation without the computational overhead of processing raw images during training.

By providing the coordinate data directly, this dataset allows researchers and developers to bypass the MediaPipe extraction pipeline and jump straight into model architecture and training.

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Classes (29 Total)

The dataset includes both the standard alphabet and functional communication commands:

• Alphabets (26 classes): A through Z
• Functional Commands (3 classes): space, speak, stop

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Dataset Structure (Zip Archive)

To ensure fast and reliable downloading, the dataset is packaged as a single compressed archive:

media_pipe_keypoints_dataset.zip

Once extracted, the folder contains no predefined train/test split. It is structured into 29 distinct class folders, allowing researchers to implement custom validation strategies such as K-Fold Cross Validation or custom random splits.

Folder Structure

media_pipe_keypoints_dataset/
├── A/
├── B/
├── C/
├── ...
├── Z/
├── space/
├── speak/
└── stop/

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How to Use This Dataset in Python

Since the dataset is zipped, here is a quick snippet to download and extract it directly in your Python code or Jupyter Notebook.

from huggingface_hub import hf_hub_download
import zipfile
import os

# 1. Download the zip file from Hugging Face
zip_path = hf_hub_download(
    repo_id="om192006/sign_language_keypoints",  # Replace with your exact repo ID
    filename="media_pipe_keypoints_dataset.zip",
    repo_type="dataset"
)

# 2. Unzip the dataset into a local folder
extract_dir = "./sign_language_data"
os.makedirs(extract_dir, exist_ok=True)

with zipfile.ZipFile(zip_path, 'r') as zip_ref:
    zip_ref.extractall(extract_dir)

print(f"Dataset extracted successfully to: {extract_dir}")

# You can now iterate through the A-Z folders inside extract_dir

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Potential Use Cases

Real-time Gesture Translation

Training sequential or dense neural networks to classify gestures from live webcam feeds.

Accessibility Technology

Developing applications that bridge communication gaps for mute and hard-of-hearing communities.

Educational Tools

Building interactive systems to help users learn and practice sign language.

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Author

Created by Om Pradip Chougule.

Citation

If you use this dataset, please provide appropriate attribution by citing the repository in your research, projects, or publications.