DOI: https://doi.org/10.14710/jtsiskom.2021.13969

AKSALont: Aplikasi transliterasi aksara Lontar Bali dengan model LSTM

AKSALont: Automatic transliteration application for Balinese palm leaf manuscripts with LSTM Model

Virtual, Vision, Image, and Pattern Research Group, Department of Informatics Engineering, Faculty of Engineering and Vocational, Universitas Pendidikan Ganesha. Jl. Udayana No. 11, Singaraja, Bali 81116, Indonesia

Received: 3 Nov 2020; Revised: 3 May 2021; Accepted: 15 May 2021; Available online: 15 Jun 2021; Published: 31 Jul 2021.
Open Access Copyright (c) 2021 The Authors. Published by Department of Computer Engineering, Universitas Diponegoro
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Citation Format:
Abstract
This study aims to develop an automatic transliteration application for the Balinese palm leaf manuscripts into the Latin/Roman alphabet. The input for this system is the digital image of the original text from the ancient Balinese palm leaf manuscripts, not from the Balinese script, which is printed using a font on a computer. In this study, a segmentation-free transliteration machine using the LSTM model was implemented. In addition, the implementation of the AKSALont application is carried out for the interactions on a web-based platform using cross-platform interoperability. The experimental results show that the machine can transliterate Balinese characters on the Balinese palm-leaf manuscript images properly with a CER of 19.78 % using 10.475 test data. With a web-based online platform, AKSALont has been able to open wider access for the public to the web-based content with an online platform collection.
Fulltext View|Download Email colleagues
Keywords: transliteration; Balinese palm leaf manuscripts; LSTM model; web-based platform
Funding: DRPM DIKTI melalui Skema Penelitian Dasar Unggulan Perguruan Tinggi (PDUPT) Tahun 2020

Article Metrics:

Article Info
Section: Original Research Articles
Language : ID
Most viewed articles
Implementasi Algoritma Kriptografi RSA untuk Enkripsi dan Dekripsi Email Pembuatan Sistem Informasi Rental Mobil dengan Menggunakan Java dan Mysql Optimization for prediction model of palm oil land suitability using spatial decision tree algorithm Implementasi Sistem Informasi Geografis Daerah Pariwisata Kota Semarang Berbasis Android dengan Global Positioning System (GPS) Pembuatan Aplikasi Antar-Jemput Laundry Berbasis Web Service pada Platform Android More articles
Most cited articles
Landfill Gas Monitoring System Based on Internet of Things Prediction of Call Drops in GSM Network using Artificial Neural Network Location Based Service Panduan Pencarian Rumah Sakit dengan Platform Android di Kota Semarang Automatic Home Roof System on Smarthome by Using Arduino Clothing size recommender on real-time fitting simulation using skeleton tracking and rigging More cited articles
  1. R. Chamchong and C. C. Fung, “Character segmentation from ancient palm leaf manuscripts in Thailand,” in 2011 Workshop on Historical Document Imaging and Processing, Beijing, China, Sept. 2011, pp. 140-145. doi: 10.1145/2037342.2037366
  2. R. Chamchong and C. C. Fung, “Text line extraction using adaptive partial projection for palm leaf manuscripts from Thailand,” in 2012 International Conference on Frontiers in Handwriting Recognition, Bari, Italy, Sep. 2012, pp. 588–593. doi: 10.1109/ICFHR.2012.280
  3. D. Valy, M. Verleysen, S. Chhun, and J.-C. Burie, “A new Khmer palm leaf manuscript dataset for document analysis and recognition – Sleukrith set,” in 4th International Workshop on Historical Document Imaging and Processing, Kyoto, Japan, Nov. 2017, pp. 1-6. doi: 10.1145/3151509.3151510
  4. D. Valy, M. Verleysen, and K. Sok, “Line segmentation approach for ancient palm leaf manuscripts using competitive learning algorithm,” in 15th International Conference on Frontiers in Handwriting Recognition, Shenzhen, China, Oct. 2016, pp. 108-113. doi: 10.1109/ICFHR.2016.0032
  5. M. W. A. Kesiman, J. C. Burie, J. M. Ogier, and P. Grangé, “Knowledge and phonological rules for the automatic transliteration of Balinese Script on palm leaf manuscript,” Computación y Sistemas, vol. 21, no. 4, pp. 1-9, 2018. doi: 10.13053/cys-21-4-2851
  6. M. W. A. Kesiman, J.-C. Burie, and J.-M. Ogier, “A complete scheme of spatially categorized glyph recognition for the transliteration of Balinese palm leaf manuscripts,” in IAPR International Conference on Document Analysis and Recognition (ICDAR), Kyoto, Japan, Nov. 2017, pp. 125–130. doi: 10.1109/ICDAR.2017.29
  7. D. Doermann and K. Tombre, Eds., Handbook of document image processing and recognition. London: Springer London, 2014. doi: 10.1007/978-0-85729-859-1
  8. M. W. A. Kesiman, “Word recognition for the Balinese palm leaf manuscripts,” in EEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom), Banda Aceh, Indonesia, Aug. 2019, pp. 72–76. doi: 10.1109/CYBERNETICSCOM.2019.8875634
  9. M. W. A. Kesiman, S. Prum, J.-C. Burie, and J.-M. Ogier, “Study on feature extraction methods for character recognition of Balinese script on palm leaf manuscript images,” in 23rd International Conference on Pattern Recognition, Cancun, Mexico, Dec. 2016, pp. 4017-4022. doi: 10.1109/ICPR.2016.7900262
  10. M. Kesiman et al., “Benchmarking of document image analysis tasks for palm leaf manuscripts from Southeast Asia,” Journal of Imaging, vol. 4, no. 2, 43, 2018. doi: 10.3390/jimaging4020043
  11. Tom, ocropy: Python-based tools for document analysis and OCR. 2018
  12. M. Suryani, E. Paulus, S. Hadi, U. A. Darsa, and J.-C. Burie, “The handwritten Sundanese palm leaf manuscript dataset from 15th century,” in IAPR International Conference on Document Analysis and Recognition, Kyoto, Japan, Nov. 2017, pp. 796-800. doi: 10.1109/ICDAR.2017.135
  13. P. Shishtla, V. S. Ganesh, S. Subramaniam, and V. Varma, “A language-independent transliteration schema using character aligned models at NEWS 2009,” in 2009 Named Entities Workshop: Shared Task on Transliteration, Suntec, Singapore, Aug. 2009, pp. 40-43. doi: 10.3115/1699705.1699715
  14. N. AbdulJaleel and L. S. Larkey, “English to Arabic transliteration for information retrieval: a statistical approach,” in International Conference on Information and Knowledge Management, Orleans, USA, Nov. 2004, pp. 139-146
  15. A. Finch and E. Sumita, “Transliteration using a phrase-based statistical machine translation system to re-score the output of a joint multigram model,” in Named Entities Workshop, Uppsala, Sweden, Jul. 2010, pp. 48–52
  16. L. Pretkalnina, P. Paikens, N. Gruzitis, L. Rituma, and A. Spektors, “Making historical latvian texts more intelligible to contemporary readers,” in LREC Workshop on Adaptation of Language Resources and Tools for Processing Cultural Heritage Objects, Istanbul, Turkey, May 2012, pp. 1-7
  17. T. M. Breuel, A. Ul-Hasan, M. A. Al-Azawi, and F. Shafait, “High-Performance OCR for printed English and fraktur using LSTM networks,” in International Conference on Document Analysis and Recognition, Washington, USA, Aug. 2013, pp. 683–687. doi: 10.1109/ICDAR.2013.140
  18. M. Jenckel, S. S. Bukhari, and A. Dengel, “anyOCR: A sequence learning based OCR system for unlabeled historical documents,” in International Conference on Pattern Recognition, Cancun, Mexico, Dec. 2016, pp. 4035–4040. doi: 10.1109/ICPR.2016.7900265
  19. A. Ul-Hasan and T. M. Breuel, “Can we build language-independent OCR using LSTM networks?,” in International Workshop on Multilingual OCR, Washington, USA, Aug. 2013, pp. 1-5. doi: 10.1145/2505377.2505394
  20. M. W. A. Kesiman, J.-C. Burie, J.-M. Ogier, G. N. M. A. Wibawantara, and I. M. G. Sunarya, “AMADI_LontarSet: the first handwritten Balinese palm leaf manuscripts dataset,” in International Conference on Frontiers in Handwriting Recognition, Shenzhen, China, Oct. 2016, pp. 168–172. doi: 10.1109/ICFHR.2016.0042

Last update:

  1. Human motion recognition information processing system based on LSTM Recurrent Neural Network Algorithm

    Xieji li, Xia Cao. Journal of Ambient Intelligence and Humanized Computing, 14 (7), 2023. doi: 10.1007/s12652-021-03614-x

  2. Financial Crisis Prediction Based on Long-Term and Short-Term Memory Neural Network

    Tang Ling, Yinying Cai, Zhiguo Qu. Wireless Communications and Mobile Computing, 2022 , 2022. doi: 10.1155/2022/5728470

Last update: 2024-11-19 20:40:03

No citation recorded.