DeepLontar dataset for handwritten Balinese character detection and syllable recognition on Lontar manuscript

Daniel Siahaan; Ni Putu Sutramiani; Nanik Suciati; I Nengah Duija; I Wayan Agus Surya Darma

doi:10.1038/s41597-022-01867-5

Scientific Data (Dec 2022)

DeepLontar dataset for handwritten Balinese character detection and syllable recognition on Lontar manuscript

Daniel Siahaan,
Ni Putu Sutramiani,
Nanik Suciati,
I Nengah Duija,
I Wayan Agus Surya Darma

Affiliations

Daniel Siahaan: Department of Informatics, Faculty of Intelligent Electrical and Informatics Technology, Institut Teknologi Sepuluh Nopember
Ni Putu Sutramiani: Department of Informatics, Faculty of Intelligent Electrical and Informatics Technology, Institut Teknologi Sepuluh Nopember
Nanik Suciati: Department of Informatics, Faculty of Intelligent Electrical and Informatics Technology, Institut Teknologi Sepuluh Nopember
I Nengah Duija: Department of Balinese Language Education, Postgraduate, Universitas Hindu Negeri I Gusti Bagus Sugriwa
I Wayan Agus Surya Darma: Department of Informatics, Faculty of Intelligent Electrical and Informatics Technology, Institut Teknologi Sepuluh Nopember

DOI: https://doi.org/10.1038/s41597-022-01867-5
Journal volume & issue: Vol. 9, no. 1
pp. 1 – 7

Abstract

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Measurement(s) accuracy • Precision • Recall • F1-score Technology Type(s) Python Factor Type(s) bounding box • frequency • spatial location • dimensional size Sample Characteristic - Organism lontar • manuscript • glyphs Sample Characteristic - Environment balinese glyphs Sample Characteristic - Location Bali Province

Published in Scientific Data

ISSN: 2052-4463 (Online)
Publisher: Nature Portfolio
Country of publisher: United Kingdom
LCC subjects: Science
Website: https://www.nature.com/sdata/

About the journal