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Sign language detection dataset: A resource for AI-based recognition systems.

Bindu Garg¹, Manisha Kasar¹, Priyanka Paygude¹

¹Bharati Vidyapeeth (Deemed to be University) College of Engineering, Pune, India.

|June 19, 2025

Summary

This summary is machine-generated.

This study introduces a deep learning model for automatic sign language detection, achieving high accuracy in classifying hand gestures. The developed system shows great potential for real-world applications benefiting the deaf community.

Keywords:

American Sign Language Convolutional Neural Network Deep Learning Sign Language Recognition

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Area of Science:

Computer Science
Artificial Intelligence
Human-Computer Interaction

Background:

Sign language is crucial for communication among deaf and hard-of-hearing individuals.
Automatic sign language detection systems can significantly enhance accessibility and inclusion.

Purpose of the Study:

To develop and evaluate a deep learning model for automatic sign language detection.
To classify hand gestures into distinct signs using a Convolutional Neural Network (CNN).

Main Methods:

A dataset of 26,000 sign language images was curated, with 3,000 images per alphabet letter.
Data preprocessing included resizing, grayscale conversion, normalization, and augmentation techniques (rotation, flipping, scaling, brightness adjustment, Gaussian noise).
The dataset was split into 70% training, 15% validation, and 15% testing sets to train a CNN model.

Main Results:

The CNN model demonstrated high accuracy in classifying hand gestures.
Data augmentation techniques improved model robustness against various environmental conditions.
The diverse dataset, featuring varied participants and controlled collection methods, enhanced model generalization.

Conclusions:

The developed deep learning model shows significant potential for real-world applications.
This technology can serve as valuable accessibility tools for the deaf community.
The system can be utilized for educational purposes and real-time sign language recognition.