Egyptian Sign Language Recognition Using CNN and LSTM

• URL: http://arxiv.org/abs/2107.13647v1
• Date: Wed, 28 Jul 2021 21:33:35 GMT
• Title: Egyptian Sign Language Recognition Using CNN and LSTM
• Authors: Ahmed Elhagry, Rawan Gla
• Abstract summary: We present a computer vision system with two different neural networks architectures. The two models achieved an accuracy of 90% and 72%, respectively. We examined the power of these two architectures to distinguish between 9 common words (with similar signs) among some deaf people community in Egypt.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Sign language is a set of gestures that deaf people use to communicate. Unfortunately, normal people don't understand it, which creates a communication gap that needs to be filled. Because of the variations in (Egyptian Sign Language) ESL from one region to another, ESL provides a challenging research problem. In this work, we are providing applied research with its video-based Egyptian sign language recognition system that serves the local community of deaf people in Egypt, with a moderate and reasonable accuracy. We present a computer vision system with two different neural networks architectures. The first is a Convolutional Neural Network (CNN) for extracting spatial features. The CNN model was retrained on the inception mod. The second architecture is a CNN followed by a Long Short-Term Memory (LSTM) for extracting both spatial and temporal features. The two models achieved an accuracy of 90% and 72%, respectively. We examined the power of these two architectures to distinguish between 9 common words (with similar signs) among some deaf people community in Egypt.

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