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Proceedings Paper

Information retrieval for OCR documents: a content-based probabilistic correction model
Author(s): Rong Jin; ChangXiang Zhai; Alexander Hauptmann
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Paper Abstract

The difficulty with information retrieval for OCR documents lies in the fact that OCR documents contain a significant amount of erroneous words and unfortunately most information retrieval techniques rely heavily on word matching between documents and queries. In this paper, we propose a general content-based correction model that can work on top of an existing OCR correction tool to “boost” retrieval performance. The basic idea of this correction model is to exploit the whole content of a document to supplement any other useful information provided by an existing OCR correction tool for word corrections. Instead of making an explicit correction decision for each erroneous word as typically done in a traditional approach, we consider the uncertainties in such correction decisions and compute an estimate of the original “uncorrupted” document language model accordingly. The document language model can then be used for retrieval with a language modeling retrieval approach. Evaluation using the TREC standard testing collections indicates that our method significantly improves the performance compared with simple word correction approaches such as using only the top ranked correction.

Paper Details

Date Published: 13 January 2003
PDF: 8 pages
Proc. SPIE 5010, Document Recognition and Retrieval X, (13 January 2003); doi: 10.1117/12.472838
Show Author Affiliations
Rong Jin, Carnegie Mellon Univ. (United States)
ChangXiang Zhai, Carnegie Mellon Univ. (United States)
Alexander Hauptmann, Carnegie Mellon Univ. (United States)


Published in SPIE Proceedings Vol. 5010:
Document Recognition and Retrieval X
Tapas Kanungo; Elisa H. Barney Smith; Jianying Hu; Paul B. Kantor, Editor(s)

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