Papers by Kenessary Koishybay
Evaluation of Manual and Non-manual Components for Sign Language Recognition (2020.lrec-1)
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Medet Mukushev, Arman Sabyrov, Alfarabi Imashev, Kenessary Koishybay, Vadim Kimmelman, Anara Sandygulova
| Challenge: | Deaf communities communicate via sign languages to express meaning and intent. |
| Approach: | They used sign samples from 20 commonly used signs in Kazakh-Russian Sign Language (K-RSL) to investigate whether non-manual components would improve sign’s recognition accuracy. |
| Outcome: | The results showed that using non-manual components would improve sign recognition accuracy. |
Cyrillic-MNIST: a Cyrillic Version of the MNIST Dataset (2022.lrec-1)
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| Challenge: | Existing datasets for computer vision to distinguish printed or handwritten characters in digital images are limited to one language. |
| Approach: | They propose to use a Cyrillic version of the MNIST dataset to analyze handwritten letters. |
| Outcome: | The proposed dataset is compared to the Extended MNIST (EMNIST) dataset and is available at https://github.com/bolattleubayev/cmnist. |