Classification of Document Image Blocks Using MCR Stroke Index

AbdelMalek B.C. ZIDOURI; Supoj CHINVEERAPHAN; Makoto SATO

Classification of Document Image Blocks Using MCR Stroke Index

AbdelMalek B.C. ZIDOURI, Supoj CHINVEERAPHAN, Makoto SATO

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In this paper we introduce a new feature called stroke index for document image analysis. It is based on the minimum covering run expression method (MCR). This stroke index is a function of the number of horizontal and vertical runs in the original image and of number of runs by the MCR expression. As document images may present a variety of patterns such as graph, text or picture, it is necessary for image understanding to classify these different patterns into categories beforehand. Here we show how one could use this stroke index for such applications as classification or segmentation. It also gives an insight on the possibility of stroke extraction from document images in addition to classifying different patterns in a compound image.

Publication: IEICE TRANSACTIONS on Information Vol.E78-D No.3 pp.290-294

Publication Date: 1995/03/25

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Type of Manuscript: LETTER

Category: Image Processing, Computer Graphics and Pattern Recognition

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AbdelMalek B.C. ZIDOURI, Supoj CHINVEERAPHAN, Makoto SATO, "Classification of Document Image Blocks Using MCR Stroke Index" in IEICE TRANSACTIONS on Information, vol. E78-D, no. 3, pp. 290-294, March 1995, doi: .
Abstract: In this paper we introduce a new feature called stroke index for document image analysis. It is based on the minimum covering run expression method (MCR). This stroke index is a function of the number of horizontal and vertical runs in the original image and of number of runs by the MCR expression. As document images may present a variety of patterns such as graph, text or picture, it is necessary for image understanding to classify these different patterns into categories beforehand. Here we show how one could use this stroke index for such applications as classification or segmentation. It also gives an insight on the possibility of stroke extraction from document images in addition to classifying different patterns in a compound image.
URL: https://global.ieice.org/en_transactions/information/10.1587/e78-d_3_290/_p

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@ARTICLE{e78-d_3_290,
author={AbdelMalek B.C. ZIDOURI, Supoj CHINVEERAPHAN, Makoto SATO, },
journal={IEICE TRANSACTIONS on Information},
title={Classification of Document Image Blocks Using MCR Stroke Index},
year={1995},
volume={E78-D},
number={3},
pages={290-294},
abstract={In this paper we introduce a new feature called stroke index for document image analysis. It is based on the minimum covering run expression method (MCR). This stroke index is a function of the number of horizontal and vertical runs in the original image and of number of runs by the MCR expression. As document images may present a variety of patterns such as graph, text or picture, it is necessary for image understanding to classify these different patterns into categories beforehand. Here we show how one could use this stroke index for such applications as classification or segmentation. It also gives an insight on the possibility of stroke extraction from document images in addition to classifying different patterns in a compound image.},
keywords={},
doi={},
ISSN={},
month={March},}

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TY - JOUR
TI - Classification of Document Image Blocks Using MCR Stroke Index
T2 - IEICE TRANSACTIONS on Information
SP - 290
EP - 294
AU - AbdelMalek B.C. ZIDOURI
AU - Supoj CHINVEERAPHAN
AU - Makoto SATO
PY - 1995
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E78-D
IS - 3
JA - IEICE TRANSACTIONS on Information
Y1 - March 1995
AB - In this paper we introduce a new feature called stroke index for document image analysis. It is based on the minimum covering run expression method (MCR). This stroke index is a function of the number of horizontal and vertical runs in the original image and of number of runs by the MCR expression. As document images may present a variety of patterns such as graph, text or picture, it is necessary for image understanding to classify these different patterns into categories beforehand. Here we show how one could use this stroke index for such applications as classification or segmentation. It also gives an insight on the possibility of stroke extraction from document images in addition to classifying different patterns in a compound image.
ER -