IEEE Trans Image Process - Text segmentation for MRC document compression.

Tópicos

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Resumo

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 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 upon the segmentation algorithm used to compute the binary mask. In this paper, we propose a novel multiscale segmentation scheme for MRC document encoding based upon 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 selected segmentation algorithms in the literature, we show that the new algorithm achieves greater accuracy of text detection but with a lower false detection rate of nontext features. We also demonstrate that the proposed segmentation algorithm can improve the quality of decoded documents while simultaneously lowering the bit rate.

Resumo Limpo

mix raster content mrc standard itut t specifi framework document compress can dramat improv compressionqu tradeoff compar tradit lossi imag compress algorithm key mrc compress separ document foreground background layer repres binari mask therefor result qualiti compress ratio mrc document encod high depend upon segment algorithm use comput binari mask paper propos novel multiscal segment scheme mrc document encod base upon sequenti applic two algorithm first algorithm cost optim segment cos blockwis segment algorithm formul global cost optim framework second algorithm connect compon classif ccc refin initi segment classifi featur vector connect compon use markov random field mrf model combin cosccc segment algorithm incorpor multiscal framework order improv segment accuraci text vari size comparison stateoftheart commerci mrc product select segment algorithm literatur show new algorithm achiev greater accuraci text detect lower fals detect rate nontext featur also demonstr propos segment algorithm can improv qualiti decod document simultan lower bit rate

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