An Adaptive Segmentation Algorithm for Degraded Chinese Rubbing Image Binarization Based on Background Estimation

Han Huang; Zhi-Kai Huang; Yong-Li Ma; Ling-Ying Hou

doi:10.1007/978-3-319-95930-6_3

An Adaptive Segmentation Algorithm for Degraded Chinese Rubbing Image Binarization Based on Background Estimation

Han Huang^*, Zhi-Kai Huang, Yong-Li Ma, Ling-Ying Hou

^*Corresponding author for this work

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

2 Citations (Scopus)

Abstract

Image Segmentation plays an important role in image processing and analysis. In order to preserve strokes of a Chinese character while enhancing character details for degraded historical document image, we propose an adaptive segmentation algorithm for degraded historical document image binarization based on background estimation for non-uniform illumination images. The novelty of the proposed method is that find an optimal background estimation based on Blind/Referenceless Image Spatial QUality Evaluator. The proposed method has four steps: (i) preprocess using median filtering; (ii) extraction of the red color components; (iii) a morphological operation in order to find an optimal background estimation; and (iv) segmented binary image using Otsu’s Thresholding. Experimental results demonstrate that it is capable of extracting more accurate segmentation of characters for degraded Chinese rubbing document image.

Original language	English
Title of host publication	Intelligent Computing Theories and Application
Subtitle of host publication	14th International Conference, ICIC 2018, Proceedings
Editors	De-Shuang Huang, Vitoantonio Bevilacqu, Prashan Premaratne, Phalguni Gupta
Publisher	Springer, Cham
Pages	15-24
Volume	1
ISBN (Electronic)	978-3-319-95930-6
ISBN (Print)	978-3-319-95929-0
DOIs	https://doi.org/10.1007/978-3-319-95930-6_3
Publication status	Published - Aug 2018
Event	14th International Conference on Intelligent Computing, ICIC 2018 - Wuhan, China Duration: 15 Aug 2018 → 18 Aug 2018 http://ic-ic.tongji.edu.cn/2018/index.htm

Publication series

Name	Lecture Notes in Computer Science (including subseries Information Systems and Applications, incl. Internet/Web, and HCI)
Publisher	Springer, Cham
Volume	LNCS 10954
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	14th International Conference on Intelligent Computing, ICIC 2018
Country/Territory	China
City	Wuhan
Period	15/08/18 → 18/08/18
Internet address	http://ic-ic.tongji.edu.cn/2018/index.htm

Research Keywords

Background estimation
Blind/referenceless image spatial quality evaluator
Mathematical morphology
Top-hat transform

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1007/978-3-319-95930-6_3

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Huang, H., Huang, Z.-K., Ma, Y.-L., & Hou, L.-Y. (2018). An Adaptive Segmentation Algorithm for Degraded Chinese Rubbing Image Binarization Based on Background Estimation. In D.-S. Huang, V. Bevilacqu, P. Premaratne, & P. Gupta (Eds.), Intelligent Computing Theories and Application: 14th International Conference, ICIC 2018, Proceedings (Vol. 1, pp. 15-24). (Lecture Notes in Computer Science (including subseries Information Systems and Applications, incl. Internet/Web, and HCI); Vol. LNCS 10954). Springer, Cham. https://doi.org/10.1007/978-3-319-95930-6_3

Huang, Han ; Huang, Zhi-Kai ; Ma, Yong-Li et al. / An Adaptive Segmentation Algorithm for Degraded Chinese Rubbing Image Binarization Based on Background Estimation. Intelligent Computing Theories and Application: 14th International Conference, ICIC 2018, Proceedings. editor / De-Shuang Huang ; Vitoantonio Bevilacqu ; Prashan Premaratne ; Phalguni Gupta. Vol. 1 Springer, Cham, 2018. pp. 15-24 (Lecture Notes in Computer Science (including subseries Information Systems and Applications, incl. Internet/Web, and HCI)).

@inproceedings{0165b5ea2ec1438287ea4e97656723a8,

title = "An Adaptive Segmentation Algorithm for Degraded Chinese Rubbing Image Binarization Based on Background Estimation",

abstract = "Image Segmentation plays an important role in image processing and analysis. In order to preserve strokes of a Chinese character while enhancing character details for degraded historical document image, we propose an adaptive segmentation algorithm for degraded historical document image binarization based on background estimation for non-uniform illumination images. The novelty of the proposed method is that find an optimal background estimation based on Blind/Referenceless Image Spatial QUality Evaluator. The proposed method has four steps: (i) preprocess using median filtering; (ii) extraction of the red color components; (iii) a morphological operation in order to find an optimal background estimation; and (iv) segmented binary image using Otsu{\textquoteright}s Thresholding. Experimental results demonstrate that it is capable of extracting more accurate segmentation of characters for degraded Chinese rubbing document image.",

keywords = "Background estimation, Blind/referenceless image spatial quality evaluator, Mathematical morphology, Top-hat transform",

author = "Han Huang and Zhi-Kai Huang and Yong-Li Ma and Ling-Ying Hou",

year = "2018",

month = aug,

doi = "10.1007/978-3-319-95930-6_3",

language = "English",

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series = "Lecture Notes in Computer Science (including subseries Information Systems and Applications, incl. Internet/Web, and HCI)",

publisher = "Springer, Cham",

pages = "15--24",

editor = "De-Shuang Huang and Vitoantonio Bevilacqu and Prashan Premaratne and Phalguni Gupta",

booktitle = "Intelligent Computing Theories and Application",

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Huang, H, Huang, Z-K, Ma, Y-L & Hou, L-Y 2018, An Adaptive Segmentation Algorithm for Degraded Chinese Rubbing Image Binarization Based on Background Estimation. in D-S Huang, V Bevilacqu, P Premaratne & P Gupta (eds), Intelligent Computing Theories and Application: 14th International Conference, ICIC 2018, Proceedings. vol. 1, Lecture Notes in Computer Science (including subseries Information Systems and Applications, incl. Internet/Web, and HCI), vol. LNCS 10954, Springer, Cham, pp. 15-24, 14th International Conference on Intelligent Computing, ICIC 2018, Wuhan, China, 15/08/18. https://doi.org/10.1007/978-3-319-95930-6_3

An Adaptive Segmentation Algorithm for Degraded Chinese Rubbing Image Binarization Based on Background Estimation. / Huang, Han; Huang, Zhi-Kai; Ma, Yong-Li et al.
Intelligent Computing Theories and Application: 14th International Conference, ICIC 2018, Proceedings. ed. / De-Shuang Huang; Vitoantonio Bevilacqu; Prashan Premaratne; Phalguni Gupta. Vol. 1 Springer, Cham, 2018. p. 15-24 (Lecture Notes in Computer Science (including subseries Information Systems and Applications, incl. Internet/Web, and HCI); Vol. LNCS 10954).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - An Adaptive Segmentation Algorithm for Degraded Chinese Rubbing Image Binarization Based on Background Estimation

AU - Huang, Han

AU - Huang, Zhi-Kai

AU - Ma, Yong-Li

AU - Hou, Ling-Ying

PY - 2018/8

Y1 - 2018/8

N2 - Image Segmentation plays an important role in image processing and analysis. In order to preserve strokes of a Chinese character while enhancing character details for degraded historical document image, we propose an adaptive segmentation algorithm for degraded historical document image binarization based on background estimation for non-uniform illumination images. The novelty of the proposed method is that find an optimal background estimation based on Blind/Referenceless Image Spatial QUality Evaluator. The proposed method has four steps: (i) preprocess using median filtering; (ii) extraction of the red color components; (iii) a morphological operation in order to find an optimal background estimation; and (iv) segmented binary image using Otsu’s Thresholding. Experimental results demonstrate that it is capable of extracting more accurate segmentation of characters for degraded Chinese rubbing document image.

AB - Image Segmentation plays an important role in image processing and analysis. In order to preserve strokes of a Chinese character while enhancing character details for degraded historical document image, we propose an adaptive segmentation algorithm for degraded historical document image binarization based on background estimation for non-uniform illumination images. The novelty of the proposed method is that find an optimal background estimation based on Blind/Referenceless Image Spatial QUality Evaluator. The proposed method has four steps: (i) preprocess using median filtering; (ii) extraction of the red color components; (iii) a morphological operation in order to find an optimal background estimation; and (iv) segmented binary image using Otsu’s Thresholding. Experimental results demonstrate that it is capable of extracting more accurate segmentation of characters for degraded Chinese rubbing document image.

KW - Background estimation

KW - Blind/referenceless image spatial quality evaluator

KW - Mathematical morphology

KW - Top-hat transform

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U2 - 10.1007/978-3-319-95930-6_3

DO - 10.1007/978-3-319-95930-6_3

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 978-3-319-95929-0

VL - 1

T3 - Lecture Notes in Computer Science (including subseries Information Systems and Applications, incl. Internet/Web, and HCI)

SP - 15

EP - 24

BT - Intelligent Computing Theories and Application

A2 - Huang, De-Shuang

A2 - Bevilacqu, Vitoantonio

A2 - Premaratne, Prashan

A2 - Gupta, Phalguni

PB - Springer, Cham

T2 - 14th International Conference on Intelligent Computing, ICIC 2018

Y2 - 15 August 2018 through 18 August 2018

ER -

Huang H, Huang ZK, Ma YL, Hou LY. An Adaptive Segmentation Algorithm for Degraded Chinese Rubbing Image Binarization Based on Background Estimation. In Huang DS, Bevilacqu V, Premaratne P, Gupta P, editors, Intelligent Computing Theories and Application: 14th International Conference, ICIC 2018, Proceedings. Vol. 1. Springer, Cham. 2018. p. 15-24. (Lecture Notes in Computer Science (including subseries Information Systems and Applications, incl. Internet/Web, and HCI)). Epub 2018 Jul 6. doi: 10.1007/978-3-319-95930-6_3

An Adaptive Segmentation Algorithm for Degraded Chinese Rubbing Image Binarization Based on Background Estimation

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