Abstract: This paper discusses the bigram model with 3-operation Edit Distance (Levenshtein Distance) String matching metrics for translation retrieval in Hindi-English Translation memory system. In this method we used the statistical language modeling (N-gram approach) to compute bigrams and then implemented the dynamic programming algorithm Levenshtein Distance to find the minimum number of edit operations required transforming one bigram to another and will act as a measure to provide extent for the matching of current input and the source in the TM. This measure will decide whether the translation retrieved correspondingly will be exact match or fuzzy match. Other string matching approaches are evaluated with Levenshtein Distance proving to be more effective comparatively.

Keywords: String Matching, Bigram modelling, Levenshtein Distance, Hindi-English Translation memory (TM).