Signclusive Mediapipe
Signclusive Mediapipe
Datasets
Signclusive Mediapipe
File
Signclusive Mediapipe
Use Case
Signclusive Mediapipe
Description
Discover the Signclusive Mediapipe dataset: 13,500 images of hand signs for all letters and the space sign, collected from five diverse signers.
Description:
The Signclusive Mediapipe dataset is a comprehensive collection designed for the development and training of machine learning models in recognizing sign language. This dataset encompasses images representing the 26 letters of the English alphabet, as well as the “space” sign, making a total of 27 distinct classes. Each class is represented by images from five different signers, ensuring diversity in hand shapes and styles.
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The data collection process for the Signclusive Mediapipe dataset involved gathering images from five different signers, each contributing 100 samples per letter of the alphabet and the “space” sign, resulting in 500 samples per letter and a total of 13,500 images. Each image was originally captured at a resolution of 640×480 pixels and later resized to 224×224 pixels to meet the input requirements for neural network training. This resizing ensured consistency and compatibility across the dataset. Additionally, the images were processed using the Mediapipe framework to detect and highlight hand landmarks, with the background blacked out to focus the model’s attention solely on the hand features, enhancing the model’s ability to accurately recognize and learn from the sign language gestures.
Dataset Composition and Structure
The dataset consists of 13,500 images, with each class containing 500 samples. In particular, the data is collected from five different individuals, each contributing 100 images per class. The dataset includes:
- 26 alphabet classes (A–Z)
- 1 additional class representing “space”
- Balanced distribution across all categories
Data Collection and Preprocessing
The images were originally captured at a resolution of 640×480 pixels. However, they were resized to 224×224 pixels to match the input requirements of deep learning models. As a result, the dataset becomes compatible with popular architectures such as CNN-based models.
Furthermore, the Mediapipe framework was used to detect hand landmarks. In addition, the background was removed or darkened to highlight the hand region. Consequently, this preprocessing step helps models focus on essential features and improves recognition accuracy.
Key Features of the Dataset
- 13,500 labeled images for robust training
- 27 distinct gesture classes
- Multiple signers for better diversity
- Preprocessed images with highlighted hand regions
- Optimized resolution for deep learning models
Applications and Use Cases
The Signclusive Mediapipe Dataset can be used in various real-world applications. For example:
- Sign Language Recognition Systems: Enable communication for hearing-impaired individuals
- Human-Computer Interaction: Develop gesture-based control systems
- AI Assistive Technologies: Improve accessibility solutions
- Computer Vision Research: Train and benchmark gesture recognition models
Conclusion
The Signclusive Mediapipe Dataset is a valuable resource for developing accurate sign language recognition models. Overall, it offers a balanced, diverse, and well-processed dataset for machine learning applications. More importantly, it contributes to building inclusive AI solutions that enhance communication and accessibility.
This dataset is sourced from Kaggle.
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