Segmenting an address into meaningful components, also known as address parsing, is an essential step in many applications from record linkage to geocoding and package delivery. Consequently, a lot of work has been dedicated to develop accurate address parsing techniques, with machine learning and neural network methods leading the state-of-the-art scoreboard. However, most of the work on address parsing has been confined to academic endeavours with little availability of free and easy-to-use open-source solutions. This paper presents Deepparse, a Python open-source, extendable, fine-tunable address parsing solution under LGPL-3.0 licence to parse multinational addresses using state-of-the-art deep learning algorithms and evaluated on over 60 countries. It can parse addresses written in any language and use any address standard. The pre-trained model achieves average $99~\%$ parsing accuracies on the countries used for training with no pre-processing nor post-processing needed. Moreover, the library supports fine-tuning with new data to generate a custom address parser.

Editor's note: Dave Currie joined Lob's Atlas team in June 2020 as a remote contractor. Working as the team's Machine Learning Engineer, he has helped to improve the accuracy of the Address Verification product by developing microservices that utilize machine learning. This article was written about one of these microservices. When I tell people that my work is focused on improving an address verification product, I sometimes receive confused looks. If you think about a friend's address, you might picture something like "1600 Pennsylvania Avenue, Washington, DC 20500".