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Proceedings Paper

Match graph generation for symbolic indirect correlation
Author(s): Daniel Lopresti; George Nagy; Ashutosh Joshi
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Paper Abstract

Symbolic indirect correlation (SIC) is a new approach for bringing lexical context into the recognition of unsegmented signals that represent words or phrases in printed or spoken form. One way of viewing the SIC problem is to find the correspondence, if one exists, between two bipartite graphs, one representing the matching of the two lexical strings and the other representing the matching of the two signal strings. While perfect matching cannot be expected with real-world signals and while some degree of mismatch is allowed for in the second stage of SIC, such errors, if they are too numerous, can present a serious impediment to a successful implementation of the concept. In this paper, we describe a framework for evaluating the effectiveness of SIC match graph generation and examine the relatively simple, controlled cases of synthetic images of text strings typeset, both normally and in highly condensed fashion. We quantify and categorize the errors that arise, as well as present a variety of techniques we have developed to visualize the intermediate results of the SIC process.

Paper Details

Date Published: 16 January 2006
PDF: 9 pages
Proc. SPIE 6067, Document Recognition and Retrieval XIII, 606706 (16 January 2006); doi: 10.1117/12.651827
Show Author Affiliations
Daniel Lopresti, Lehigh Univ. (United States)
George Nagy, Rensselaer Polytechnic Institute (United States)
Ashutosh Joshi, Rensselaer Polytechnic Institute (United States)


Published in SPIE Proceedings Vol. 6067:
Document Recognition and Retrieval XIII
Kazem Taghva; Xiaofan Lin, Editor(s)

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