"Document(page_content='LayoutParser : A Uni\\x0ced Toolkit for Deep\\nLearning Based Document Image Analysis\\nZejiang Shen1( \\x00), Ruochen Zhang2, Melissa Dell3, Benjamin Charles Germain\\nLee4, Jacob Carlson3, and Weining Li5\\n1Allen Institute for AI\\nshannons@allenai.org\\n2Brown University\\nruochen zhang@brown.edu\\n3Harvard University\\nfmelissadell,jacob carlson g@fas.harvard.edu\\n4University of Washington\\nbcgl@cs.washington.edu\\n5University of Waterloo\\nw422li@uwaterloo.ca\\nAbstract. Recent advances in document image analysis (DIA) have been\\nprimarily driven by the application of neural networks. Ideally, research\\noutcomes could be easily deployed in production and extended for further\\ninvestigation. However, various factors like loosely organized codebases\\nand sophisticated model con\\x0cgurations complicate the easy reuse of im-\\nportant innovations by a wide audience. Though there have been on-going\\ne\\x0borts to improve reusability and simplify deep learning (DL) model\\ndevelopment in disciplines like natural language processing and computer\\nvision, none of them are optimized for challenges in the domain of DIA.\\nThis represents a major gap in the existing toolkit, as DIA is central to\\nacademic research across a wide range of disciplines in the social sciences\\nand humanities. This paper introduces LayoutParser , an open-source\\nlibrary for streamlining the usage of DL in DIA research and applica-\\ntions. The core LayoutParser library comes with a set of simple and\\nintuitive interfaces for applying and customizing DL models for layout de-\\ntection, character recognition, and many other document processing tasks.\\nTo promote extensibility, LayoutParser also incorporates a community\\nplatform for sharing both pre-trained models and full document digiti-\\nzation pipelines. We demonstrate that LayoutParser is helpful for both\\nlightweight and large-scale digitization pipelines in real-word use cases.\\nThe library is publicly available at https://layout-parser.github.io .\\nKeywords: Document Image Analysis ·Deep Learning ·Layout Analysis\\n·Character Recognition ·Open Source library ·Toolkit.\\n1 Introduction\\nDeep Learning(DL)-based approaches are the state-of-the-art for a wide range of\\ndocument image analysis (DIA) tasks including document image classi\\x0ccation [ 11,arXiv:2103.15348v2 [cs.CV] 21 Jun 2021', lookup_str='', metadata={'source': 'example_data/layout-parser-paper.pdf', 'page': '0'}, lookup_index=0)"
"Efficient Data AnnotationC u s t o m i z e d M o d e l T r a i n i n gModel Cust omizationDI A Model HubDI A Pipeline SharingCommunity PlatformLa y out Detection ModelsDocument Images \n",
"T h e C o r e L a y o u t P a r s e r L i b r a r yOCR ModuleSt or age & VisualizationLa y out Data Structur e\n",
"Fig. 1: The overall architecture of LayoutParser . For an input document image,\n",
"the core LayoutParser library provides a set of o\u000b",
"-the-shelf tools for layout\n",
"detection, OCR, visualization, and storage, backed by a carefully designed layout\n",
"data structure. LayoutParser also supports high level customization via e\u000ecient\n",
"layout annotation and model training functions. These improve model accuracy\n",
"on the target samples. The community platform enables the easy sharing of DIA\n",
"models and whole digitization pipelines to promote reusability and reproducibility.\n",
"A collection of detailed documentation, tutorials and exemplar projects make\n",
"LayoutParser easy to learn and use.\n",
"AllenNLP [ 8] and transformers [ 34] have provided the community with complete\n",
"DL-based support for developing and deploying models for general computer\n",
"vision and natural language processing problems. LayoutParser , on the other\n",
"hand, specializes speci\f",
"cally in DIA tasks. LayoutParser is also equipped with a\n",
"community platform inspired by established model hubs such as Torch Hub [23]\n",
"andTensorFlow Hub [1]. It enables the sharing of pretrained models as well as\n",
"full document processing pipelines that are unique to DIA tasks.\n",
"There have been a variety of document data collections to facilitate the\n",
"development of DL models. Some examples include PRImA [ 3](magazine layouts),\n",
"PubLayNet [ 38](academic paper layouts), Table Bank [ 18](tables in academic\n",
"Under the hood, Unstructured creates different \"elements\" for different chunks of text. By default we combine those together, but you can easily keep that separation by specifying `mode=\"elements\"`."