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Daniel P. Lopresti

“Match Graph Generation for Symbolic Indirect Correlation” (with G. Nagy and Ashutosh Joshi, Document Recognition and Retrieval XIII (IS&T/SPIE International Symposium on Electronic Imaging), January 2006, San Jose, CA.

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  (PDF 334 kbytes)

© 2004 P.C. Rossin College of Engineering & Applied Science
Computer Science & Engineering, Packard Laboratory, Lehigh University, Bethlehem PA 18015