Evaluating Table Structure Recognition: A New Perspective

Existing metrics used to evaluate table structure recognition algorithms have shortcomings with regard to capturing text and empty cells alignment. In this paper, we build on prior work and propose a new metric - TEDS based IOU similarity (TEDS (IOU)) for table structure recognition which uses bound...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Hauptverfasser:	Kumar, Tarun, Bhatt, Himanshu Sharad
Format:	Artikel
Sprache:	eng
Schlagworte:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Learning
Online-Zugang:	Volltext bestellen
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

container_end_page
container_issue
container_start_page
container_title
container_volume
creator	Kumar, Tarun Bhatt, Himanshu Sharad
description	Existing metrics used to evaluate table structure recognition algorithms have shortcomings with regard to capturing text and empty cells alignment. In this paper, we build on prior work and propose a new metric - TEDS based IOU similarity (TEDS (IOU)) for table structure recognition which uses bounding boxes instead of text while simultaneously being robust against the above disadvantages. We demonstrate the effectiveness of our metric against previous metrics through various examples.
doi_str_mv	10.48550/arxiv.2208.00385
format	Article
fullrecord	<record><control><sourceid>arxiv_GOX</sourceid><recordid>TN_cdi_arxiv_primary_2208_00385</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2208_00385</sourcerecordid><originalsourceid>FETCH-LOGICAL-a675-b0871decdf50a2bdb2daa9c266f281b619813937b2b33a6bb81d8953499a3f443</originalsourceid><addsrcrecordid>eNotj0tuwjAUAL3poqI9QFf1BRL8iR2bHUL0IyFAJfvoPdtBlkJAxknb21elXc1uNEPIE2dlZZRic0hfcSqFYKZkTBp1T-x6gn6EHIcjbQD7QA85jS6PKdCP4M7HIeZ4HhZ0Sbfhk-5Dul6Cy3EKD-Sug_4aHv85I83Lulm9FZvd6_tquSlA16pAZmrug_OdYiDQo_AA1gmtO2E4am4Nl1bWKFBK0IiGe2OVrKwF2VWVnJHnP-2tvb2keIL03f4-tLcH-QPeFUDx</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>Evaluating Table Structure Recognition: A New Perspective</title><source>arXiv.org</source><creator>Kumar, Tarun ; Bhatt, Himanshu Sharad</creator><creatorcontrib>Kumar, Tarun ; Bhatt, Himanshu Sharad</creatorcontrib><description>Existing metrics used to evaluate table structure recognition algorithms have shortcomings with regard to capturing text and empty cells alignment. In this paper, we build on prior work and propose a new metric - TEDS based IOU similarity (TEDS (IOU)) for table structure recognition which uses bounding boxes instead of text while simultaneously being robust against the above disadvantages. We demonstrate the effectiveness of our metric against previous metrics through various examples.</description><identifier>DOI: 10.48550/arxiv.2208.00385</identifier><language>eng</language><subject>Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Learning</subject><creationdate>2022-07</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,778,883</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2208.00385$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2208.00385$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Kumar, Tarun</creatorcontrib><creatorcontrib>Bhatt, Himanshu Sharad</creatorcontrib><title>Evaluating Table Structure Recognition: A New Perspective</title><description>Existing metrics used to evaluate table structure recognition algorithms have shortcomings with regard to capturing text and empty cells alignment. In this paper, we build on prior work and propose a new metric - TEDS based IOU similarity (TEDS (IOU)) for table structure recognition which uses bounding boxes instead of text while simultaneously being robust against the above disadvantages. We demonstrate the effectiveness of our metric against previous metrics through various examples.</description><subject>Computer Science - Computer Vision and Pattern Recognition</subject><subject>Computer Science - Learning</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotj0tuwjAUAL3poqI9QFf1BRL8iR2bHUL0IyFAJfvoPdtBlkJAxknb21elXc1uNEPIE2dlZZRic0hfcSqFYKZkTBp1T-x6gn6EHIcjbQD7QA85jS6PKdCP4M7HIeZ4HhZ0Sbfhk-5Dul6Cy3EKD-Sug_4aHv85I83Lulm9FZvd6_tquSlA16pAZmrug_OdYiDQo_AA1gmtO2E4am4Nl1bWKFBK0IiGe2OVrKwF2VWVnJHnP-2tvb2keIL03f4-tLcH-QPeFUDx</recordid><startdate>20220731</startdate><enddate>20220731</enddate><creator>Kumar, Tarun</creator><creator>Bhatt, Himanshu Sharad</creator><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20220731</creationdate><title>Evaluating Table Structure Recognition: A New Perspective</title><author>Kumar, Tarun ; Bhatt, Himanshu Sharad</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a675-b0871decdf50a2bdb2daa9c266f281b619813937b2b33a6bb81d8953499a3f443</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Computer Science - Computer Vision and Pattern Recognition</topic><topic>Computer Science - Learning</topic><toplevel>online_resources</toplevel><creatorcontrib>Kumar, Tarun</creatorcontrib><creatorcontrib>Bhatt, Himanshu Sharad</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Kumar, Tarun</au><au>Bhatt, Himanshu Sharad</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Evaluating Table Structure Recognition: A New Perspective</atitle><date>2022-07-31</date><risdate>2022</risdate><abstract>Existing metrics used to evaluate table structure recognition algorithms have shortcomings with regard to capturing text and empty cells alignment. In this paper, we build on prior work and propose a new metric - TEDS based IOU similarity (TEDS (IOU)) for table structure recognition which uses bounding boxes instead of text while simultaneously being robust against the above disadvantages. We demonstrate the effectiveness of our metric against previous metrics through various examples.</abstract><doi>10.48550/arxiv.2208.00385</doi><oa>free_for_read</oa></addata></record>
fulltext	fulltext_linktorsrc
identifier	DOI: 10.48550/arxiv.2208.00385
ispartof
issn
language	eng
recordid	cdi_arxiv_primary_2208_00385
source	arXiv.org
subjects	Computer Science - Computer Vision and Pattern Recognition Computer Science - Learning
title	Evaluating Table Structure Recognition: A New Perspective
url	https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-16T18%3A14%3A25IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-arxiv_GOX&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Evaluating%20Table%20Structure%20Recognition:%20A%20New%20Perspective&rft.au=Kumar,%20Tarun&rft.date=2022-07-31&rft_id=info:doi/10.48550/arxiv.2208.00385&rft_dat=%3Carxiv_GOX%3E2208_00385%3C/arxiv_GOX%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true