Auto correction functionality is very popular in search portals. Its principal purpose is to correct common spelling or typing errors, saving time for the user. However, when there are millions of strings in a dictionary, it takes considerable amount of time to find the nearest matching string. Various approaches have been proposed for efficiently implementing auto correction functionality. All of these approaches focus on using suitable data structure and few heuristics to solve the problems. Here, we propose a new idea which eliminates the need for calculating edit distance with each string in the dictionary. It uses the concept of Ngram based indexing and hashing to filter out irrelevant strings from dictionary. Experiments suggest that proposed algorithm provides both efficient and accurate results.