American Journal of Artificial Intelligence

Research Article | | Peer-Reviewed |

Signed Language Translation into Afaan Oromo Text Using Deep-Learning Approach

Received: 17 October 2023    Accepted: 2 November 2023    Published: 17 November 2023
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Abstract

A person who is unable to talk or hear anything can communicate via sign language. For those who have trouble hearing, sign language is a great way to communicate their thoughts and feelings. The vocabulary, grammar, and allied lexicons of sign language are well- defined. This study focuses primarily on Signed Afaan Oromo. The main issue in our society is the detection of Sign Language for the Afaan Oromo language. The construction of static word level, alphabet, and number translations into their equivalent Afaan Oromo text is the main focus of this thesis study. Video frames are used as the system's input, and Afaan Oromo text is used as the system's ultimate output. Data from 90 classes at the alphabet, number, and word level from five special needs instructors have been collected as part of an experiment and literature study to help answer the research objectives. Preprocessing, such as frame extraction, resizing, labeling, and splitting data using Roboflow, as well as the conversion of photos into Yolo model format, was done in order to train our model. Finally, based on the results of our experiment, we can quickly and effectively recognize and classify gestures using data sets of a medium size. The image, webcam, and video file's promising value and forecast results indicate that the yolov5 algorithm has a good chance of successfully detecting the sign in real-time. We trained and tested the model using a signed Afaan Oromo dataset. The YOLOv5s model was successful in obtaining accuracy of 90%, recall of 92.5%, mAP of 93.2% at 0.5 IoU, and a score of 71.5% at 0.5:0.95 IoU, which is suitable for real-time gesture translation.

DOI 10.11648/j.ajai.20230702.12
Published in American Journal of Artificial Intelligence (Volume 7, Issue 2, December 2023)
Page(s) 40-51
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Signed Language, Deep Learning, Computer Vision, CNN, YOLOv5

References
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  • APA Style

    Negash Tesso, D., Fikadu Dinsa, E., Fikadu Kenani, H. (2023). Signed Language Translation into Afaan Oromo Text Using Deep-Learning Approach. American Journal of Artificial Intelligence, 7(2), 40-51. https://doi.org/10.11648/j.ajai.20230702.12

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    ACS Style

    Negash Tesso, D.; Fikadu Dinsa, E.; Fikadu Kenani, H. Signed Language Translation into Afaan Oromo Text Using Deep-Learning Approach. Am. J. Artif. Intell. 2023, 7(2), 40-51. doi: 10.11648/j.ajai.20230702.12

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    AMA Style

    Negash Tesso D, Fikadu Dinsa E, Fikadu Kenani H. Signed Language Translation into Afaan Oromo Text Using Deep-Learning Approach. Am J Artif Intell. 2023;7(2):40-51. doi: 10.11648/j.ajai.20230702.12

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  • @article{10.11648/j.ajai.20230702.12,
      author = {Diriba Negash Tesso and Etana Fikadu Dinsa and Hawi Fikadu Kenani},
      title = {Signed Language Translation into Afaan Oromo Text Using Deep-Learning Approach},
      journal = {American Journal of Artificial Intelligence},
      volume = {7},
      number = {2},
      pages = {40-51},
      doi = {10.11648/j.ajai.20230702.12},
      url = {https://doi.org/10.11648/j.ajai.20230702.12},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajai.20230702.12},
      abstract = {A person who is unable to talk or hear anything can communicate via sign language. For those who have trouble hearing, sign language is a great way to communicate their thoughts and feelings. The vocabulary, grammar, and allied lexicons of sign language are well- defined. This study focuses primarily on Signed Afaan Oromo. The main issue in our society is the detection of Sign Language for the Afaan Oromo language. The construction of static word level, alphabet, and number translations into their equivalent Afaan Oromo text is the main focus of this thesis study. Video frames are used as the system's input, and Afaan Oromo text is used as the system's ultimate output. Data from 90 classes at the alphabet, number, and word level from five special needs instructors have been collected as part of an experiment and literature study to help answer the research objectives. Preprocessing, such as frame extraction, resizing, labeling, and splitting data using Roboflow, as well as the conversion of photos into Yolo model format, was done in order to train our model. Finally, based on the results of our experiment, we can quickly and effectively recognize and classify gestures using data sets of a medium size. The image, webcam, and video file's promising value and forecast results indicate that the yolov5 algorithm has a good chance of successfully detecting the sign in real-time. We trained and tested the model using a signed Afaan Oromo dataset. The YOLOv5s model was successful in obtaining accuracy of 90%, recall of 92.5%, mAP of 93.2% at 0.5 IoU, and a score of 71.5% at 0.5:0.95 IoU, which is suitable for real-time gesture translation.
    },
     year = {2023}
    }
    

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  • TY  - JOUR
    T1  - Signed Language Translation into Afaan Oromo Text Using Deep-Learning Approach
    AU  - Diriba Negash Tesso
    AU  - Etana Fikadu Dinsa
    AU  - Hawi Fikadu Kenani
    Y1  - 2023/11/17
    PY  - 2023
    N1  - https://doi.org/10.11648/j.ajai.20230702.12
    DO  - 10.11648/j.ajai.20230702.12
    T2  - American Journal of Artificial Intelligence
    JF  - American Journal of Artificial Intelligence
    JO  - American Journal of Artificial Intelligence
    SP  - 40
    EP  - 51
    PB  - Science Publishing Group
    SN  - 2639-9733
    UR  - https://doi.org/10.11648/j.ajai.20230702.12
    AB  - A person who is unable to talk or hear anything can communicate via sign language. For those who have trouble hearing, sign language is a great way to communicate their thoughts and feelings. The vocabulary, grammar, and allied lexicons of sign language are well- defined. This study focuses primarily on Signed Afaan Oromo. The main issue in our society is the detection of Sign Language for the Afaan Oromo language. The construction of static word level, alphabet, and number translations into their equivalent Afaan Oromo text is the main focus of this thesis study. Video frames are used as the system's input, and Afaan Oromo text is used as the system's ultimate output. Data from 90 classes at the alphabet, number, and word level from five special needs instructors have been collected as part of an experiment and literature study to help answer the research objectives. Preprocessing, such as frame extraction, resizing, labeling, and splitting data using Roboflow, as well as the conversion of photos into Yolo model format, was done in order to train our model. Finally, based on the results of our experiment, we can quickly and effectively recognize and classify gestures using data sets of a medium size. The image, webcam, and video file's promising value and forecast results indicate that the yolov5 algorithm has a good chance of successfully detecting the sign in real-time. We trained and tested the model using a signed Afaan Oromo dataset. The YOLOv5s model was successful in obtaining accuracy of 90%, recall of 92.5%, mAP of 93.2% at 0.5 IoU, and a score of 71.5% at 0.5:0.95 IoU, which is suitable for real-time gesture translation.
    
    VL  - 7
    IS  - 2
    ER  - 

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Author Information
  • Department of Computer Science, College of Engineering and Technology, Wallaga University, Nekemte, Ethiopia

  • Department of Computer Science, College of Engineering and Technology, Wallaga University, Nekemte, Ethiopia

  • Department of Computer Science, College of Engineering and Technology, Wallaga University, Nekemte, Ethiopia

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