  Address matching is an important task for many businesses especially delivery
and take out companies which help them to take out a certain address from their
data warehouse. Existing solution uses similarity of strings, and edit distance
algorithms to find out the similar addresses from the address database, but
these algorithms could not work effectively with redundant, unstructured, or
incomplete address data. This paper discuss semantic Address matching
technique, by which we can find out a particular address from a list of
possible addresses. We have also reviewed existing practices and their
shortcoming. Semantic address matching is an essentially NLP task in the field
of deep learning. Through this technique We have the ability to triumph the
drawbacks of existing methods like redundant or abbreviated data problems. The
solution uses the OCR on invoices to extract the address and create the data
pool of addresses. Then this data is fed to the algorithm BM-25 for scoring the
best matching entries. Then to observe the best result, this will pass through
BERT for giving the best possible result from the similar queries. Our
investigation exhibits that our methodology enormously improves both accuracy
and review of cutting-edge technology existing techniques.
