Markov Random Field Model Based Text Segmentation and Image Post Processing of Complex Scanned Documents

Haneda, Eri Ph.D., Purdue University, May 2011. Markov Random Field Model Based Text Segmentation and Image Post Processing of Complex Scanned Documents. Major Professor: Charles A. Bouman. In this dissertation, two image processing studies will be presented. The first study is segmentation for MRC document compression using an MRF model, and the second study is an automatic contrast enhancement scheme for a digital image capture device. In the first study, we developed a new document segmentation scheme for Mixed Raster Content (MRC) standard. The Mixed Raster Content 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 compression is the separation of the document into foreground and background layers, represented as a binary mask. Therefore, the resulting quality and compression ratio of a MRC document encoder is highly dependent on the segmentation algorithm used to compute the binary mask. In this study, we propose a novel multiscale segmentation scheme for MRC document encoding 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 an 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. In comparisons to state-of-the-art commercial MRC products and se-