Texture Detection for Letter Carving Segmentation of Ancient Copper Inscriptions

Susijanto T. Rasmana; Yoyon K. Suprapto; I. Ketut Eddy Purnama; Keiichi Uchimura; Gou Koutaki

doi:10.1142/S0218001417550023

Texture Detection for Letter Carving Segmentation of Ancient Copper Inscriptions

Susijanto T. Rasmana, Yoyon K. Suprapto, I. Ketut Eddy Purnama, Keiichi Uchimura, Gou Koutaki

Research output: Contribution to journal › Review article › peer-review

2 Citations (Scopus)

Abstract

As relics of history, ancient copper inscriptions are found in many countries. Information in the image or letter forms contained on copper ancient inscription has a very high value. The age and environmental factors caused damage to the surface of the inscription and also reduced the appearances of the image and letter. In this paper, we describe a novel segmentation methodology based on multi-texture features for ancient copper inscriptions which were severely damaged. The segmentation results of letters on ancient copper inscriptions by using the proposed method have an average accuracy of 90%. Based on these results, the proposed method is suitable for letter segmentation of the ancient copper inscriptions.

Original language	English
Article number	1755002
Journal	International Journal of Pattern Recognition and Artificial Intelligence
Volume	31
Issue number	1
DOIs	https://doi.org/10.1142/S0218001417550023
Publication status	Published - 1 Jan 2017

Keywords

FO
GLCM
SVM
Segmentation
texture feature

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title = "Texture Detection for Letter Carving Segmentation of Ancient Copper Inscriptions",

abstract = "As relics of history, ancient copper inscriptions are found in many countries. Information in the image or letter forms contained on copper ancient inscription has a very high value. The age and environmental factors caused damage to the surface of the inscription and also reduced the appearances of the image and letter. In this paper, we describe a novel segmentation methodology based on multi-texture features for ancient copper inscriptions which were severely damaged. The segmentation results of letters on ancient copper inscriptions by using the proposed method have an average accuracy of 90%. Based on these results, the proposed method is suitable for letter segmentation of the ancient copper inscriptions.",

keywords = "FO, GLCM, SVM, Segmentation, texture feature",

author = "Rasmana, {Susijanto T.} and Suprapto, {Yoyon K.} and Purnama, {I. Ketut Eddy} and Keiichi Uchimura and Gou Koutaki",

note = "Publisher Copyright: {\textcopyright} 2017 World Scientific Publishing Company.",

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doi = "10.1142/S0218001417550023",

language = "English",

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TY - JOUR

T1 - Texture Detection for Letter Carving Segmentation of Ancient Copper Inscriptions

AU - Rasmana, Susijanto T.

AU - Suprapto, Yoyon K.

AU - Purnama, I. Ketut Eddy

AU - Uchimura, Keiichi

AU - Koutaki, Gou

PY - 2017/1/1

Y1 - 2017/1/1

N2 - As relics of history, ancient copper inscriptions are found in many countries. Information in the image or letter forms contained on copper ancient inscription has a very high value. The age and environmental factors caused damage to the surface of the inscription and also reduced the appearances of the image and letter. In this paper, we describe a novel segmentation methodology based on multi-texture features for ancient copper inscriptions which were severely damaged. The segmentation results of letters on ancient copper inscriptions by using the proposed method have an average accuracy of 90%. Based on these results, the proposed method is suitable for letter segmentation of the ancient copper inscriptions.

AB - As relics of history, ancient copper inscriptions are found in many countries. Information in the image or letter forms contained on copper ancient inscription has a very high value. The age and environmental factors caused damage to the surface of the inscription and also reduced the appearances of the image and letter. In this paper, we describe a novel segmentation methodology based on multi-texture features for ancient copper inscriptions which were severely damaged. The segmentation results of letters on ancient copper inscriptions by using the proposed method have an average accuracy of 90%. Based on these results, the proposed method is suitable for letter segmentation of the ancient copper inscriptions.

KW - FO

KW - GLCM

KW - SVM

KW - Segmentation

KW - texture feature

UR - http://www.scopus.com/inward/record.url?scp=84979243000&partnerID=8YFLogxK

U2 - 10.1142/S0218001417550023

DO - 10.1142/S0218001417550023

M3 - Review article

AN - SCOPUS:84979243000

SN - 0218-0014

VL - 31

JO - International Journal of Pattern Recognition and Artificial Intelligence

JF - International Journal of Pattern Recognition and Artificial Intelligence

IS - 1

M1 - 1755002

ER -

Texture Detection for Letter Carving Segmentation of Ancient Copper Inscriptions

Abstract

Keywords

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