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OCR errors hurt retrieval performance to a great extent. Research has been done on modelling and correction  of OCR errors. However, most of the existing systems use language dependent resources or training texts  for studying the nature of errors. Not much research has been reported on improving retrieval performance  from erroneous text when no training data is available. We propose an algorithm of detecting OCR errors and  improving retrieval performance from the erroneous corpus. We present two versions of the algorithm: one  based on word cooccurrence and the other based on Pointwise Mutual Information. Our algorithm does not  use any training data or any language specific resources like thesaurus. It also does not use any knowledge  about the language except that the word delimiter is a blank space. We have tested our algorithm on erroneous  Bangla FIRE collection and obtained significant improvements.

A Word Association Based Approach for Improving Retrieval Performance from Noisy OCRed Text