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Sign and Human Action Detection Using Deep Learning.

Shivanarayna Dhulipala¹, Festus Fatai Adedoyin¹, Alessandro Bruno²

¹Department of Computing and Informatics, Bournemouth University, Talbot Campus Poole, Poole BH12 5BB, UK.

Journal of Imaging

|July 25, 2022

Summary

This study developed a deep learning model to bridge the communication gap between sign language and spoken language users. The Convolutional Neural Network (CNN) model achieved high accuracy in recognizing British Sign Language, outperforming the Long Short-Term Memory (LSTM) model.

Keywords:

CNN LSTM british sign language confusion matrix precision recall

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Area of Science:

Computer Science
Artificial Intelligence
Linguistics

Background:

Effective communication is vital for human interaction and dispute resolution.
A significant communication barrier exists between users of sign language and spoken language.
Bridging this gap can enhance community inclusion for speech-impaired individuals.

Purpose of the Study:

To develop an efficient deep learning model for British Sign Language (BSL) recognition.
To reduce the communication disparity between speech-impaired and non-speech-impaired individuals.
To evaluate the performance of different deep learning architectures for BSL prediction.

Main Methods:

Development of two deep learning models: Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM).
Evaluation of model performance using a multi-class confusion matrix.
Quantitative analysis of training and testing accuracies, precision, and recall.

Main Results:

The CNN model demonstrated superior performance with training accuracy of 98.8% and testing accuracy of 97.4%.
CNN achieved high average weighted precision (97%) and recall (96%).
The LSTM model exhibited significantly lower performance, with maximum accuracies around 49%.

Conclusions:

The CNN model is highly effective for recognizing and interpreting British Sign Language.
Deep learning approaches, particularly CNNs, show great promise in overcoming sign language communication barriers.
This research contributes to improved communication accessibility for the deaf and hard-of-hearing community.