A Content-based Probabilistic Correction Model for OCR Document Retrieval

The difficulty with information retrieval for OCR documents lies in the fact that OCR documents comprise of 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 correction. 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 a TREC standard testing collection indicates that our method significantly improves the performance when compared with simple word correction approaches such as using only the top ranked correction.