NDLI: Page segmentation using minimum homogeneity algorithm and adaptive mathematical morphology

Content Provider	Springer Nature Link
Author	Tran, Tuan Anh Na, In Seop Kim, Soo Hyung
Copyright Year	2016
Abstract	Document layout analysis or page segmentation is the task of decomposing document images into many different regions such as texts, images, separators, and tables. It is still a challenging problem due to the variety of document layouts. In this paper, we propose a novel hybrid method, which includes three main stages to deal with this problem. In the first stage, the text and non-text elements are classified by using minimum homogeneity algorithm. This method is the combination of connected component analysis and multilevel homogeneity structure. Then, in the second stage, a new homogeneity structure is combined with an adaptive mathematical morphology in the text document to get a set of text regions. Besides, on the non-text document, further classification of non-text elements is applied to get separator regions, table regions, image regions, etc. The final stage, in refinement region and noise detection process, all regions both in the text document and non-text document are refined to eliminate noises and get the geometric layout of each region. The proposed method has been tested with the dataset of ICDAR2009 page segmentation competition and many other databases with different languages. The results of these tests showed that our proposed method achieves a higher accuracy compared to other methods. This proves the effectiveness and superiority of our method.
Starting Page	191
Ending Page	209
Page Count	19
File Format	PDF
ISSN	14332833
Journal	International Journal of Document Analysis and Recognition (IJDAR)
Volume Number	19
Issue Number	3
e-ISSN	14332825
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2016-03-11
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Page segmentation Document layout analysis Homogeneity structure OCR Mathematical morphology Recursive filter Image Processing and Computer Vision Pattern Recognition
Content Type	Text
Resource Type	Article
Subject	Computer Science Applications Computer Vision and Pattern Recognition Software

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