A soft computing model for mapping incomplete/approximate postal addresses to mail delivery points

Mapping of postal address to a mail delivery point is a very important task that affects the efficiency of postal service. This task is very complex in the countries such as India, where postal addresses are not structured. Further most of the times the destination addresses in such countries are incomplete, approximate and erroneous which adds to the complexity of mapping postal address to delivery point. Automation of this aspect of the postal service is a challenge. This paper presents a soft computing model to map the postal address to mail delivery point. Firstly machine readable postal address is processed to identify the address components using a novel fuzzy symbolic similarity analysis, and further these labeled components are organized as a symbolic postal address object. This postal address object is further processed using the newly devised fuzzy symbolic methodology for mapping the address to mail delivery point. Symbolic knowledge bases for postal address component labeling and mail delivery point mapping are devised. Fuzzy symbolic similarity measures are formulated once for address component labeling and the second time for mapping the entire address to a mail delivery point. In sequel to similarity computations, which are viewed as fuzzy membership values, an expert system comprising of α-cut de-fuzzification is proposed to evaluate the confidence factors, while inferencing the validity of address component labels and mail delivery points. The system is tested exhaustively and an efficiency of 94% is obtained in address component identification and about 86% in mail delivery point mapping, while working on an Indian postal data base of about 500 addresses.