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              <text>ML based sign language recognition system</text>
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              <text>Automated SLR; Classification models; Feature extraction; Preprocessing; Segmentation</text>
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              <text>This paper reviews different steps in an automated sign language recognition (SLR) system. Developing a system that can read and interpret a sign must be trained using a large dataset and the best algorithm. As a basic SLR system, an isolated recognition model is developed. The model is based on vision-based isolated hand gesture detection and recognition. Assessment of ML-based SLR model was conducted with the help of 4 candidates under a controlled environment. The model made use of a convex hull for feature extraction and KNN for classification. The model yielded 65% accuracy.   2021 IEEE.</text>
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              <text>Amrutha K.; Prabu P.</text>
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              <text>2021 International Conference on Innovative Trends in Information Technology, ICITIIT 2021</text>
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              <text>Institute of Electrical and Electronics Engineers Inc.</text>
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              <text>2021-01-01</text>
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              <text>&lt;a href="https://doi.org/10.1109/ICITIIT51526.2021.9399594" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/ICITIIT51526.2021.9399594&lt;/a&gt;
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              <text>ISBN: 978-166540467-9</text>
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              <text>Amrutha K., CHRIST(Deemed to Be University), Dept. of Computer Science, Bangalore, India; Prabu P., CHRIST(Deemed to Be University), Dept. of Computer Science, Bangalore, India</text>
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