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


Zejiang Shen           1  (     ), Ruochen Zhang                2, Melissa Dell         3, Benjamin Charles Germain
                                         Lee   4, Jacob Carlson            3, and Weining Li              5

                                                           1  Allen Institute for AI
                                                           shannons@allenai.org
                                                               2  Brown University
                                                        ruochen          zhang@brown.edu
                                                             3  Harvard University
                                  {melissadell,jacob                       carlson       }@fas.harvard.edu
                                                       4  University of Washington
                                                         bcgl@cs.washington.edu
                                                          5  University of Waterloo
                                                            w422li@uwaterloo.ca



             Abstract.        Recentadvancesindocumentimageanalysis(DIA)havebeen
             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 im-
             portant innovations by awide audience. Though there havebeen 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 applica-
             tions. The core          LayoutParser            library comes with a set of simple and
             intuitive interfaces for applying and customizing DL models for layout de-
             tection,characterrecognition,andmanyotherdocumentprocessingtasks.
             To promote extensibility,                 LayoutParser            also incorporates a community
             platform for sharing both pre-trained models and full document digiti-
             zation 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                            .

             Keywords:          DocumentImageAnalysis                                    ·DeepLearning                    ·LayoutAnalysis
             · Character Recognition                              · Open Source library                           · Toolkit.

1   Introduction

Deep Learning(DL)-based approaches are the state-of-the-art for a wide range of
documentimageanalysis(DIA)tasksincludingdocumentimageclassification[                                              11 ,