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Learning Disabilities

Learning Disabilities

Learning disabilities are cognitive disorders caused by neurological impairments that affect cognitive functions like language and reading, without indicating overall intellectual or developmental challenges. These disabilities differ from global intellectual or developmental disabilities as they are limited to distinct cognitive functions. Common learning disabilities include dysgraphia, dyslexia, and dyscalculia, each of which impacts unique aspects of learning.
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Deep Learning Technology to Recognize American Sign Language Alphabet.

Bader Alsharif^1,2, Ali Salem Altaher¹, Ahmed Altaher^1,3

¹Department of Electrical Engineering and Computer Science, Florida Atlantic University, 777 Glades Road, Boca Raton, FL 33431, USA.

Sensors (Basel, Switzerland)

|September 28, 2023

Summary

This study used deep learning models to recognize American Sign Language (ASL) alphabet hand gestures. ResNet-50 achieved 99.98% accuracy, significantly improving communication tools for the hearing impaired.

Keywords:

AlexNet American sign language ConvNeXt EfficientNet ResNet-50 VisionTransformer deep learning image-based transfer learning

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Area of Science:

Computer Science
Artificial Intelligence
Human-Computer Interaction

Background:

Historically, communication barriers have impacted individuals with hearing impairments.
Modern technology offers potential solutions for enhancing communication accessibility.

Purpose of the Study:

To develop and evaluate deep learning models for recognizing American Sign Language (ASL) alphabet hand gestures.
To bridge the communication gap between deaf/hard-of-hearing and hearing individuals through technology.

Main Methods:

Utilized five deep learning models: AlexNet, ConvNeXt, EfficientNet, ResNet-50, and VisionTransformer.
Trained and tested models on a dataset of over 87,000 ASL alphabet hand gesture images.
Conducted experiments with architectural parameter modifications to optimize recognition accuracy.

Main Results:

ResNet-50 achieved the highest accuracy at 99.98%.
EfficientNet reached 99.95% accuracy.
ConvNeXt and AlexNet showed strong performance with 99.51% and 99.50% accuracy, respectively.
VisionTransformer achieved 88.59% accuracy.

Conclusions:

Deep learning models, particularly ResNet-50, demonstrate high efficacy in recognizing ASL hand gestures.
These advancements can lead to improved communication tools for the hearing-impaired community.
Further research can explore real-time applications and broader sign language recognition.