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Recent advances in document image analysis (DIA) have been primarily drivenby the application of neural networks. Ideally, research outcomes could beeasily deployed in production and extended for further investigation. However,various factors like loosely organized codebases and sophisticated modelconfigurations complicate the easy reuse of important innovations by a wideaudience. Though there have been on-going efforts to improve reusability andsimplify deep learning (DL) model development in disciplines like naturallanguage processing and computer vision, none of them are optimized forchallenges in the domain of DIA. This represents a major gap in the existingtoolkit, as DIA is central to academic research across a wide range ofdisciplines in the social sciences and humanities. This paper introduceslayoutparser, an open-source library for streamlining the usage of DL in DIAresearch and applications. The core layoutparser library comes with a set ofsimple and intuitive interfaces for applying and customizing DL models forlayout detection, character recognition, and many other document processingtasks. To promote extensibility, layoutparser also incorporates a communityplatform for sharing both pre-trained models and full document digitizationpipelines. We demonstrate that layoutparser is helpful for both lightweight andlarge-scale digitization pipelines in real-word use cases. The library ispublicly available at https://layout-parser.github.io/.

LayoutParser: A Unified Toolkit for Deep Learning Based Document Image Analysis