Arabic alphabet sign language gestures captured in RGB image format across multiple character classes. This collection facilitates the development of image classification models for Arabic Sign Language (ArSL) recognition.
Use Cases
- Train a deep learning model for image classification using the RGB gesture images and alphabet labels
- Build a translation layer that converts visual Arabic signs into digital text characters
- Perform data augmentation experiments on RGB hand gesture images to improve ArSL recognition accuracy
Strengths
- Includes RGB color images representing hand gestures for the Arabic alphabet
- Categorized by Arabic alphabet characters for classification labels
- Image-based data format suitable for computer vision pipelines