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Segmenting an address into meaningful components, also known as addressparsing, is an essential step in many applications from record linkage togeocoding and package delivery. Consequently, a lot of work has been dedicatedto develop accurate address parsing techniques, with machine learning andneural network methods leading the state-of-the-art scoreboard. However, mostof the work on address parsing has been confined to academic endeavours withlittle availability of free and easy-to-use open-source solutions.  This paper presents Deepparse, a Python open-source, extendable, fine-tunableaddress parsing solution under LGPL-3.0 licence to parse multinationaladdresses using state-of-the-art deep learning algorithms and evaluated on over60 countries. It can parse addresses written in any language and use anyaddress standard. The pre-trained model achieves average $99~\%$ parsingaccuracies on the countries used for training with no pre-processing norpost-processing needed. Moreover, the library supports fine-tuning with newdata to generate a custom address parser.

Deepparse : An Extendable, and Fine-Tunable State-Of-The-Art Library for Parsing Multinational Street Addresses