An Evaluation of Hand-Based Algorithms for Sign Language Recognition

Marais, Marc; Brown, Dane; Connan, James; Boby, Alden

doi:10.1109/icabcd54961.2022.9856310

Cited by 10 publications

(7 citation statements)

References 15 publications

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“…As can be seen from Table 9, the proposed feature fusion-based method outperformed the existing works in the literature using the LSA64 dataset both in E1 and E2, which are signer-independent evaluations, and in E3, which is a signer-dependent evaluation. Compared to the method proposed by Marais et al [46] based on the InceptionV3-GRU method using the entire video image with signers 5 and 10 separated as a test, our R3(2+1)D-SLR model achieved a recognition accuracy of 94.99% with an increase of 5.44% for the same input modality. This shows the superiority of our proposed deep learning model over InceptionV3-GRU in spatial and temporal feature extraction.…”

Section: Comparison With Other Studiesmentioning

confidence: 82%

“…The model proposed by Elsayed and Fathy [44] using 3D-CNN followed by Convolutional LSTM achieved a 97.4% test accuracy for 40 categories. Marais et al [45] trained the Pruned VGG network with raw images and achieved a 95.50% test accuracy. In another study by the same authors [46], using the InceptionV3-GRU architecture, they achieved a 97.03% accuracy in signer-dependent testing and 74.22% in signer-independent testing.…”

Section: Related Literaturementioning

confidence: 99%

“…Marais et al [45] trained the Pruned VGG network with raw images and achieved a 95.50% test accuracy. In another study by the same authors [46], using the InceptionV3-GRU architecture, they achieved a 97.03% accuracy in signer-dependent testing and 74.22% in signer-independent testing. Alyami et al [47] classified the key points extracted from the signer's hands and face using a transformer-based model.…”

Section: Related Literaturementioning

confidence: 99%

“…The results are shown in Table 9. [39] 98.09 [40] 99.84 Zhang et al (2019) [42] 99.063 Imran and Raman (2020) [43] 97.81 Marais et al (2022) [45] 95. 50 Alyami et al (2023) [47] 98.25 Our method (raw image-based input) 99.84 Our method (pose image-based input) 100…”

Section: Comparison With Other Studiesmentioning

confidence: 99%

“…Experiment 1 (E1): As in the study by Marais et al [46], the 5th and 10th signers were used as test data and the rest as training data.…”

mentioning

confidence: 99%

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Enhancing Signer-Independent Recognition of Isolated Sign Language through Advanced Deep Learning Techniques and Feature Fusion

Akdag,

Baykan

2024

Electronics

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Sign Language Recognition (SLR) systems are crucial bridges facilitating communication between deaf or hard-of-hearing individuals and the hearing world. Existing SLR technologies, while advancing, often grapple with challenges such as accurately capturing the dynamic and complex nature of sign language, which includes both manual and non-manual elements like facial expressions and body movements. These systems sometimes fall short in environments with different backgrounds or lighting conditions, hindering their practical applicability and robustness. This study introduces an innovative approach to isolated sign language word recognition using a novel deep learning model that combines the strengths of both residual three-dimensional (R3D) and temporally separated (R(2+1)D) convolutional blocks. The R3(2+1)D-SLR network model demonstrates a superior ability to capture the intricate spatial and temporal features crucial for accurate sign recognition. Our system combines data from the signer’s body, hands, and face, extracted using the R3(2+1)D-SLR model, and employs a Support Vector Machine (SVM) for classification. It demonstrates remarkable improvements in accuracy and robustness across various backgrounds by utilizing pose data over RGB data. With this pose-based approach, our proposed system achieved 94.52% and 98.53% test accuracy in signer-independent evaluations on the BosphorusSign22k-general and LSA64 datasets.

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Section: Comparison With Other Studiesmentioning

confidence: 82%