  Recent advances in document image analysis (DIA) have been primarily driven
by the application of neural networks. Ideally, research outcomes could be
easily deployed in production and extended for further investigation. However,
various factors like loosely organized codebases and sophisticated model
configurations complicate the easy reuse of important innovations by a wide
audience. Though there have been on-going efforts to improve reusability and
simplify deep learning (DL) model development in disciplines like natural
language processing and computer vision, none of them are optimized for
challenges in the domain of DIA. This represents a major gap in the existing
toolkit, as DIA is central to academic research across a wide range of
disciplines in the social sciences and humanities. This paper introduces
layoutparser, an open-source library for streamlining the usage of DL in DIA
research and applications. The core layoutparser library comes with a set of
simple and intuitive interfaces for applying and customizing DL models for
layout detection, character recognition, and many other document processing
tasks. To promote extensibility, layoutparser also incorporates a community
platform for sharing both pre-trained models and full document digitization
pipelines. We demonstrate that layoutparser is helpful for both lightweight and
large-scale digitization pipelines in real-word use cases. The library is
publicly available at https://layout-parser.github.io/.
