AKSALont: Automatic transliteration application for Balinese palm leaf manuscripts with LSTM Model-Reference-Cited by-同舟云学术

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

Published:2021-05-15 Issue:3 Volume:9 Page:142-149
ISSN:2338-0403
Container-title:Jurnal Teknologi dan Sistem Komputer
language:
Short-container-title:J. Teknol. dan Sist. Komput

Author:

Kesiman Made Windu Antara¹^ORCID,Dermawan Kadek Teguh¹^ORCID

Affiliation:

1. 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

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.

Funder

DRPM DIKTI melalui Skema Penelitian Dasar Unggulan Perguruan Tinggi (PDUPT) Tahun 2020

Publisher

Institute of Research and Community Services Diponegoro University (LPPM UNDIP)

Subject

General Earth and Planetary Sciences,General Environmental Science

Reference16 articles.

1. Character segmentation from ancient palm leaf manuscripts in Thailand

2. Text Line Extraction Using Adaptive Partial Projection for Palm Leaf Manuscripts from Thailand

3. A New Khmer Palm Leaf Manuscript Dataset for Document Analysis and Recognition

4. Line Segmentation Approach for Ancient Palm Leaf Manuscripts Using Competitive Learning Algorithm

5. Knowledge Representation and Phonological Rules for the Automatic Transliteration of Balinese Script on Palm Leaf Manuscript

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