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Bangla Sign Language (BdSL) Alphabets and Numerals Classification Using a Deep Learning Model.

Kanchon Kanti Podder¹, Muhammad E H Chowdhury², Anas M Tahir²

¹Department of Biomedical Physics & Technology, University of Dhaka, Dhaka 1000, Bangladesh.

Sensors (Basel, Switzerland)

|January 22, 2022

Summary

This study developed a deep learning model for Bangla Sign Language (BdSL) recognition, achieving 99.99% accuracy for alphabets and numerals. This breakthrough aims to integrate over 200,000 hearing and speech-impaired individuals into Bangladesh's workforce.

Keywords:

alphabets and numerals bangla sign language classification convolutional neural network semantic segmentation

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Published on: September 25, 2021

Area of Science:

Computer Vision
Deep Learning
Human-Computer Interaction

Background:

Bangla Sign Language (BdSL) recognition is crucial for integrating hearing and speech-impaired individuals into the workforce.
Challenges include variations in skin tone, hand orientation, and background complexity.
Existing research lacks robust datasets for BdSL alphabet and numeral recognition.

Purpose of the Study:

To develop an accurate and reliable Bangla Sign Language (BdSL) recognition system for alphabets and numerals.
To create the largest publicly available image dataset for BdSL recognition.
To compare deep learning models for optimal BdSL interpretation.

Main Methods:

Utilized deep machine learning models, including CNN and ResNet18, on two robust datasets.
Developed a comprehensive image database for BdSL alphabets and numerals, addressing inter-class similarity and diverse image data.
Compared model performance with and without background images, focusing on hand detection accuracy in segmentation.

Main Results:

The CNN model trained with background images outperformed models without backgrounds.
ResNet18 achieved superior performance with 99.99% accuracy, precision, F1 score, and sensitivity, and 100% specificity.
The developed dataset is the largest publicly available resource for BdSL alphabets and numerals.

Conclusions:

Deep learning models, particularly ResNet18, demonstrate high efficacy for Bangla Sign Language (BdSL) recognition.
The inclusion of background images in training enhances model performance.
The publicly released dataset will foster further research and development in BdSL interpretation, benefiting the hearing and speech-impaired community.