Review Article
Exploring Sign Language Detection on Smartphones: A Systematic Review of Machine and Deep Learning Approaches
Table 9
Models and their evaluation performance on specific sign languages.
| Study | Year | Model | Sign language | Results/performance |
| [154] | 2023 | 8-layer CNN | ISL | 99.34% accuracy |
| [156] | 2022 | CNN | ISL | 70.0% accuracy |
| [160] | 2021 | CNN and RNN | ISL | Top-1 (95.99%) accuracy | Top-3 (99.46%) accuracy |
| [158] | 2021 | CNN | ASL | 87.5% accuracy |
| [143] | 2022 | Built-in speakers and microphones, inaudible acoustic signal | ASL | 97.2% accuracy at word-level |
| [166] | 2021 | AutoML | ASL | 100% accuracy |
| [159] | 2021 | 3DCNN | KSL | 91.0% accuracy |
| [51] | 2016 | Cloud computing-based approach | ArSL | 77%–84% for short sentences |
| [103] | 2019 | SVM | ArSL | 92.5% accuracy |
| [49] | 2016 | Backpropagation neural network | Indonesian SL | 91.66% accuracy |
| [76] | 2018 | 2-Layer LSTM | Indonesian SL | 95.15% accuracy |
| [70] | 2018 | CNN | Indonesian SL | 100% accuracy |
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