Signet: A Deep Learning based Indian Sign Language Recognition System

Sruthi, C. J.; Lijiya, A

doi:10.1109/iccsp.2019.8698006

Cited by 25 publications

(3 citation statements)

References 21 publications

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“…Their model achieved an outstanding accuracy of 100% in off-time testing and an impressive accuracy of 97.5% in real-time testing on the ASL dataset. Similarly, C J, Sruthi et al [9] leveraged CNN and obtained a remarkable accuracy of 98.6% on the ISL dataset. Another study by K. Nimisha et al [14] involved the use of YOLO, PCA, SVM, ANN, and CNN for SLR, achieving an accuracy of 90% on the ASL dataset.…”

Section: Sign Langugae Recognition Techniques Analysismentioning

confidence: 98%

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Galgotias University, Greater Noida

KUMAR

2022

SSRN Journal

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Section: Sign Langugae Recognition Techniques Analysismentioning

confidence: 98%

“…C J Sruthi et al [9] developed a deep learning-based SLR system using CNN, achieving an accuracy of 98.6% with the ISL dataset. This approach stood out for its focus on the Indian sign language and the high accuracy achieved using a CNN-based approach.…”

Section: B Vision-based Approachmentioning

confidence: 99%

Galgotias University, Greater Noida

KUMAR

2022

SSRN Journal

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“…American sign language [24], Chinese sign language [25], British sign language [26], and Arabic sign language [27], are few examples on standardized forms of sign language. India has a wide range of sign language expressions and dialects that makes it hard to construct a standardized dictionary [28]. These issues raised how widely sign language is used on one hand, while driving the attention to the importance on focusing on the dialectical phenomena of sign language on the other.…”

Section: Introductionmentioning

confidence: 99%

Automatic recognition of Arabic alphabets sign language using deep learning

Duwairi

Halloush

2022

IJECE

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<span>Technological advancements are helping people with special needs overcome many communications’ obstacles. Deep learning and computer vision models are innovative leaps nowadays in facilitating unprecedented tasks in human interactions. The Arabic language is always a rich research area. In this paper, different deep learning models were applied to test the accuracy and efficiency obtained in automatic Arabic sign language recognition. In this paper, we provide a novel framework for the automatic detection of Arabic sign language, based on transfer learning applied on popular deep learning models for image processing. Specifically, by training AlexNet, VGGNet and GoogleNet/Inception models, along with testing the efficiency of shallow learning approaches based on support vector machine (SVM) and nearest neighbors algorithms as baselines. As a result, we propose a novel approach for the automatic recognition of Arabic alphabets in sign language based on VGGNet architecture which outperformed the other trained models. The proposed model is set to present promising results in recognizing Arabic sign language with an accuracy score of 97%. The suggested models are tested against a recent fully-labeled dataset of Arabic sign language images. The dataset contains 54,049 images, which is considered the first large and comprehensive real dataset of Arabic sign language to the furthest we know.</span>

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