27 distinct classes of American Sign Language (ASL) gestures captured from a diverse group of 173 individual participants. The dataset provides a multi-subject collection of sign language data for classification tasks across nearly thirty categories.
Use Cases
- Train a classification model to identify 27 different ASL signs using the provided class labels
- Benchmark model performance and generalization across 173 different human subjects
- Develop a sign-to-text translation interface based on the 27 gesture categories
Strengths
- Includes 27 distinct classes of American Sign Language (ASL) gestures
- Features data collected from 173 unique individuals to ensure subject diversity
- Categorized into 27 specific sign labels for supervised learning tasks