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Document Image Coding for Processing and Retrieval
Authors:Omid E. Kia and David S. Doermann
Affiliation:(1) National Institute of Standards and Technology, Mathematical and Computational Sciences Division, Building 820, Room 365, Gaithersburg, MD, 20899;(2) Language and Media Processing Laboratory, Center for Automation Research, University of Maryland, College Park, MD, 20742
Abstract:Document images belong to a unique class of images where the information is embedded in the language represented by a series of symbols on the page rather than in the visual objects themselves. Since these symbols tend to appear repeatedly, a domain-specific image coding strategy can be designed to facilitate enhanced compression and retrieval. In this paper we describe a coding methodology that not only exploits component-level redundancy to reduce code length but also supports efficient data access. The approach identifies and organizes symbol patterns which appear repeatedly. Similar components are represented by a single prototype stored in a library and the location of each component instance is coded along with the residual between it and its prototype. A representation is built which provides a natural information index allowing access to individual components. Compression results are competitive and compressed-domain access is superior to competing methods. Applications to network-related problems have been considered, and show promising results.
Keywords:
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