A document image segmentation system using analysis of connected components

被引：9
|
作者：
Zirari, F. [1 ]
Ennaji, A. [1 ]
Nicolas, S. [1 ]
Mammass, D. [2 ]
机构：
[1] Univ Rouen, LITIS Lab, Rouen, France
[2] Ibn Zohr Univ, IRF SIC Lab, Agadir, Morocco
来源：
2013 12TH INTERNATIONAL CONFERENCE ON DOCUMENT ANALYSIS AND RECOGNITION (ICDAR) | 2013年
关键词：
text/non-text separating; connected components; graph; structural analysis; document image;
D O I：
10.1109/ICDAR.2013.154
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Page segmentation into text and non-text elements is an essential preprocessing step before optical character recognition (OCR) operation. In case of poor segmentation, an OCR classification engine produces garbage characters due to the presence of non-text elements. This paper presents a method to separate the textual and non textual components in document images using a graph-based modeling and structural analysis. This is a fast and efficient method to separate adequately the graphical and the textual parts of a document. We have evaluated our method on two well-known subsets: the UW-III dataset and the ICDAR 2009 page segmentation competition dataset. Comparisons are led with two methods of state-of-the-art; these results showing that our method proved better performances in this task.
引用
收藏
页码：753 / 757
页数：5
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