Create README.md

README.md

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+---
+license: mit
+language:
+- en
+pretty_name: Sign Language MediaPipe Keypoints
+size_categories:
+- 1K<n<10K
+---
+# 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.
+
+---
+
+## 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`
+
+---
+
+## Dataset Structure (Zip Archive)
+
+To ensure fast and reliable downloading, the dataset is packaged as a single compressed archive:
+
+```text
+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
+
+```text
+media_pipe_keypoints_dataset/
+├── A/
+├── B/
+├── C/
+├── ...
+├── Z/
+├── space/
+├── speak/
+└── stop/
+```
+
+---
+
+## 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.
+
+```python
+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
+```
+
+---
+
+## 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.
+
+---
+
+## 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.