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Multiscale segmentation for MRC document compression using a Markov random field model

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2 Author(s)
Eri Haneda ; Purdue University, School of Electrical and Computer Engineering, West Lafayette, IN 47907, USA ; Charles A. Bouman

The Mixed Raster Content (MRC) standard (ITU-T T.44) specifies a framework for document compression which can dramatically improve the compression/quality tradeoff as compared to traditional lossy image compression algorithms. The key to MRC's performance is the separation of the document into foreground and background layers, represented as a binary mask. In this paper, we propose a novel multiscale segmentation scheme based on the sequential application of two algorithms. The first algorithm, Cost Optimized Segmentation (COS), is a blockwise segmentation algorithm formulated in a global cost optimization framework. The second algorithm, Connected Component Classification (CCC), refines the initial segmentation by classifying feature vectors of connected components using a Markov random field (MRF) model. The combined COS/CCC segmentation algorithms are then incorporated into a multiscale framework in order to improve the segmentation accuracy of text with varying size.

Published in:

2010 IEEE International Conference on Acoustics, Speech and Signal Processing

Date of Conference:

14-19 March 2010