Deepparse : An Extendable, and Fine-Tunable State-Of-The-Art Library for
  Parsing Multinational Street Addresses
David Beauchemin Marouane Yassine
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.