Segmentation and Contents Classification of Document Images Using Local Entropy and Texture-based PCA Algorithm


The KIPS Transactions:PartB , Vol. 16, No. 5, pp. 377-384, Oct. 2009
10.3745/KIPSTB.2009.16.5.377,   PDF Download:

Abstract

A new algorithm in order to classify various contents in the image documents, such as text, figure, graph, table, etc. is proposed in this paper by classifying contents using texture-based PCA, and by segmenting document images using local entropy-based histogram. Local entropy and histogram made the binarization of image document not only robust to various transformation and noise, but also easy and less time-consuming. And texture-based PCA algorithm for each segmented region was taken notice of each content in the image documents having different texture information. Through this, it was not necessary to establish any pre-defined structural information, and advantages were found from the fact of fast and efficient classification. The result demonstrated that the proposed method had shown better performances of segmentation and classification for various images, and is also found superior to previous methods by its efficiency.


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Cite this article
[IEEE Style]
B. R. Kim, J. T. Oh, W. H. Kim, "Segmentation and Contents Classification of Document Images Using Local Entropy and Texture-based PCA Algorithm," The KIPS Transactions:PartB , vol. 16, no. 5, pp. 377-384, 2009. DOI: 10.3745/KIPSTB.2009.16.5.377.

[ACM Style]
Bo Ram Kim, Jun Taek Oh, and Wook Hyun Kim. 2009. Segmentation and Contents Classification of Document Images Using Local Entropy and Texture-based PCA Algorithm. The KIPS Transactions:PartB , 16, 5, (2009), 377-384. DOI: 10.3745/KIPSTB.2009.16.5.377.