2022
DOI: 10.1051/e3sconf/202235101065
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Sign Language Recognition: High Performance Deep Learning Approach Applyied To Multiple Sign Languages

Abstract: In this paper we present a high performance Deep Learning architecture based on Convolutional Neural Network (CNN). The proposed architecture is effective as it is capable of recognizing and analyzing with high accuracy different Sign language datasets. The sign language recognition is one of the most important tasks that will change the lives of deaf people by facilitating their daily life and their integration into society. Our approach was trained and tested on an American Sign Language (ASL) dataset, Irish… Show more

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Cited by 5 publications
(3 citation statements)
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“…In paper [38], El Zaar et al introduced a CNN-based highly efcient deep learning architecture. Te suggested architecture is efective because it can recognize and analyze various datasets in sign language with a high degree of accuracy.…”
Section: Related Workmentioning
confidence: 99%
“…In paper [38], El Zaar et al introduced a CNN-based highly efcient deep learning architecture. Te suggested architecture is efective because it can recognize and analyze various datasets in sign language with a high degree of accuracy.…”
Section: Related Workmentioning
confidence: 99%
“…They were able to identify eight types of candlestick patterns with 90.7%. In [18], the authors introduced a deep learning-based approach to forecast trend signals and determine trading entry points. Their method combines LSTM, 1DCNN, and the XGBoost algorithm.…”
Section: Related Workmentioning
confidence: 99%
“…By incorporating advanced techniques like Recurrent Neural Networks(RNNs) and Convolutional Neural Networks(CNNs) [19] deep learning models can successfully overcome the challenges that statistical and traditional machine learning methods encounter. Consequently, this opens up new possibilities across various domains, including agriculture crop yield forecasting [20] [21].Within the realm of deep learning, RNNs, such as Long Short-Term Memory (LSTM) [22][23], Gated Recurrent Units (GRU) [24], and Bidirectional LSTM (BiLSTM) [25], are well-suited for processing sequential data and have proven to be effective in capturing long-term dependencies in time series data. They can model temporal relationships, making them particularly useful in analyzing agricultural spatiotemporal data, such as weather patterns, soil conditions, and crop growth stages.…”
Section: Introductionmentioning
confidence: 99%