Mobile Sign Language Recognition for Bahasa Indonesia using Convolutional Neural Network. Yugopuspito, P., Murwantara, I. M., & Sean, J. In Proceedings of the 16th International Conference on Advances in Mobile Computing and Multimedia, pages 84-91, Yogyakarta, Indonesia, 2018. Association for Computing Machinery (ACM).
Mobile Sign Language Recognition for Bahasa Indonesia using Convolutional Neural Network [link]Paper  doi  abstract   bibtex   
Hand gestures for speech impaired community have their usage for specific language. In Indonesia, hand gesture has their natural two hands sign and widely accepted usage, BISINDO (Bahasa Indonesia Sign Language). In this paper, we propose to use a mobile application to support people who want to communicate with speech impaired people based on BISINDO. We make use the Convolutional Neural Network method to identify the hand gesture in a real time Android mobile application. For training the image dataset, we make use of MobileNet algorithm that have satisfied us with good result, on top of a Machine Learning Framework, TensorFlow. The percentage of success has been influenced by the image reference size and the optimizer algorithm. The highest performance of implemented model reached 95.13% on its accuracy rate from 23 hand gestures from 13.802 images as dataset, and achieved 100% success on some hand gestures.
@inproceedings{yugopuspito2018mobile,
  abstract = {Hand gestures for speech impaired community have their usage for specific language. In Indonesia, hand gesture has their natural two hands sign and widely accepted usage, BISINDO (Bahasa Indonesia Sign Language). In this paper, we propose to use a mobile application to support people who want to communicate with speech impaired people based on BISINDO. We make use the Convolutional Neural Network method to identify the hand gesture in a real time Android mobile application. For training the image dataset, we make use of MobileNet algorithm that have satisfied us with good result, on top of a Machine Learning Framework, TensorFlow. The percentage of success has been influenced by the image reference size and the optimizer algorithm. The highest performance of implemented model reached 95.13% on its accuracy rate from 23 hand gestures from 13.802 images as dataset, and achieved 100% success on some hand gestures.},
  added-at = {2019-11-14T05:53:02.000+0100},
  address = {Yogyakarta, Indonesia},
  author = {Yugopuspito, Pujianto and Murwantara, I. Made and Sean, Jessica},
  biburl = {https://www.bibsonomy.org/bibtex/2df49c1ec832b26a0a93d8381a1642fa7/jpmor},
  booktitle = {Proceedings of the 16th International Conference on Advances in Mobile Computing and Multimedia},
  description = {Mobile Sign Language Recognition for Bahasa Indonesia using Convolutional Neural Network | Proceedings of the 16th International Conference on Advances in Mobile Computing and Multimedia},
  doi = {10.1145/3282353.3282356},
  eventtitle = {International Conference on Advances in Mobile Computing and Multimedia},
  interhash = {5e410d0313b4e0b672dd653af04aed2c},
  intrahash = {df49c1ec832b26a0a93d8381a1642fa7},
  keywords = {hand-gesture tensorflow android real convolutional-neural-network bahasa-indonesia},
  language = {English},
  pages = {84-91},
  publisher = {Association for Computing Machinery (ACM)},
  school = {Universitas Pelita Harapan (UPH)},
  timestamp = {2020-10-07T13:36:50.000+0200},
  title = {Mobile Sign Language Recognition for Bahasa Indonesia using Convolutional Neural Network},
  url = {https://doi.org/10.1145%2F3282353.3282356},
  year = 2018
}
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