In this paper we present an approach for correcting character recognition errors of an OCR which can recognise Indic Scripts. Suffix tree is used to index the lexicon in lexicographical order to facilitate the probabilistic search. To obtain the best probable match against the mis-recognised string, it is compared with the sub-strings (edges of suffix tree) using similarity measure as weighted Levenshtein distance, where Confusion probabilities of characters (Unicodes) are used as substitution cost, until it exceeds the specified cost k. Retrieved candidates are sorted and selected on the basis of their lowest edit cost. Exploiting this information, the system can correct nonword errors and achieves maximum error rate reduction of 33% over simple character recognition system. © 2011 IEEE.