{"id":"https://openalex.org/W3205690813","doi":"https://doi.org/10.14428/esann/2021.es2021-109","title":"TSR-DSAW: Table Structure Recognition via Deep Spatial Association of Words","display_name":"TSR-DSAW: Table Structure Recognition via Deep Spatial Association of Words","publication_year":2021,"publication_date":"2021-01-01","ids":{"openalex":"https://openalex.org/W3205690813","doi":"https://doi.org/10.14428/esann/2021.es2021-109","mag":"3205690813"},"language":"en","primary_location":{"id":"doi:10.14428/esann/2021.es2021-109","is_oa":true,"landing_page_url":"https://doi.org/10.14428/esann/2021.es2021-109","pdf_url":"https://doi.org/10.14428/esann/2021.es2021-109","source":{"id":"https://openalex.org/S4306509709","display_name":"ESANN 2021 proceedings","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ESANN 2021 proceedings","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://doi.org/10.14428/esann/2021.es2021-109","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5041506914","display_name":"Arushi Jain","orcid":"https://orcid.org/0000-0001-7556-5389"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Arushi Jain","raw_affiliation_strings":["TCS Research, Delhi, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"TCS Research, Delhi, India","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081142120","display_name":"Shubham Paliwal","orcid":"https://orcid.org/0000-0003-1532-801X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shubham Paliwal","raw_affiliation_strings":["TCS Research, Delhi, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"TCS Research, Delhi, India","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101897117","display_name":"Monika Sharma","orcid":"https://orcid.org/0000-0002-7346-2711"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Monika Sharma","raw_affiliation_strings":["TCS Research, Delhi, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"TCS Research, Delhi, India","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5071894271","display_name":"Lovekesh Vig","orcid":"https://orcid.org/0000-0001-9834-3308"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lovekesh Vig","raw_affiliation_strings":["TCS Research, Delhi, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"TCS Research, Delhi, India","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.4774,"has_fulltext":true,"cited_by_count":8,"citation_normalized_percentile":{"value":0.72789398,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"257","last_page":"262"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10601","display_name":"Handwritten Text Recognition Techniques","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10601","display_name":"Handwritten Text Recognition Techniques","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","score":0.9958999752998352,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12016","display_name":"Web Data Mining and Analysis","score":0.9940000176429749,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7210944294929504},{"id":"https://openalex.org/keywords/table","display_name":"Table (database)","score":0.694617748260498},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6522678136825562},{"id":"https://openalex.org/keywords/association","display_name":"Association (psychology)","score":0.5751993656158447},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5098478198051453},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.39943528175354004},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.39114290475845337},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.18532276153564453},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.06753107905387878}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7210944294929504},{"id":"https://openalex.org/C45235069","wikidata":"https://www.wikidata.org/wiki/Q278425","display_name":"Table (database)","level":2,"score":0.694617748260498},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6522678136825562},{"id":"https://openalex.org/C142853389","wikidata":"https://www.wikidata.org/wiki/Q744778","display_name":"Association (psychology)","level":2,"score":0.5751993656158447},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5098478198051453},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.39943528175354004},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.39114290475845337},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.18532276153564453},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.06753107905387878},{"id":"https://openalex.org/C542102704","wikidata":"https://www.wikidata.org/wiki/Q183257","display_name":"Psychotherapist","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.14428/esann/2021.es2021-109","is_oa":true,"landing_page_url":"https://doi.org/10.14428/esann/2021.es2021-109","pdf_url":"https://doi.org/10.14428/esann/2021.es2021-109","source":{"id":"https://openalex.org/S4306509709","display_name":"ESANN 2021 proceedings","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ESANN 2021 proceedings","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.14428/esann/2021.es2021-109","is_oa":true,"landing_page_url":"https://doi.org/10.14428/esann/2021.es2021-109","pdf_url":"https://doi.org/10.14428/esann/2021.es2021-109","source":{"id":"https://openalex.org/S4306509709","display_name":"ESANN 2021 proceedings","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ESANN 2021 proceedings","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3205690813.pdf","grobid_xml":"https://content.openalex.org/works/W3205690813.grobid-xml"},"referenced_works_count":17,"referenced_works":["https://openalex.org/W2015398666","https://openalex.org/W2022351003","https://openalex.org/W2074966879","https://openalex.org/W2092772700","https://openalex.org/W2111297753","https://openalex.org/W2117462434","https://openalex.org/W2150673968","https://openalex.org/W2786162033","https://openalex.org/W2810376801","https://openalex.org/W2919502278","https://openalex.org/W2947372801","https://openalex.org/W2967615747","https://openalex.org/W2968868378","https://openalex.org/W2996791521","https://openalex.org/W3003496674","https://openalex.org/W3032536818","https://openalex.org/W6760808182"],"related_works":["https://openalex.org/W2948670949","https://openalex.org/W4288047943","https://openalex.org/W2352440174","https://openalex.org/W4232484699","https://openalex.org/W4309440960","https://openalex.org/W2990655940","https://openalex.org/W2062168445","https://openalex.org/W2473636215","https://openalex.org/W1590279850","https://openalex.org/W3014091026"],"abstract_inverted_index":{"Existing":[0],"methods":[1,9,190],"for":[2,56],"Table":[3],"This":[4],"is":[5,30,119],"because":[6],"current":[7],"data-driven":[8],"work":[10],"by":[11,121],"simply":[12],"training":[13],"deep":[14,40],"models":[15],"on":[16,159,176],"large":[17],"volumes":[18],"of":[19,73,80,104,124],"data":[20],"and":[21,136,182,185,194],"fail":[22],"to":[23,37,42,144,163],"generalize":[24],"when":[25],"an":[26,63],"unseen":[27],"table":[28,54,59,82,93,166],"structure":[29,167],"encountered.":[31],"In":[32],"this":[33],"paper,":[34],"we":[35,156],"propose":[36],"train":[38],"a":[39,77,81,85,92,113,140],"network":[41,115],"capture":[43,145],"the":[44,53,58,97,102,106,110,122,160,165],"spatial":[45,146],"associations":[46,147],"between":[47],"different":[48],"word":[49],"pairs":[50],"present":[51,62,108],"in":[52,84,109,133,168],"image":[55,83,94,111],"unravelling":[57],"structure.":[60],"We":[61,171],"end-to-end":[64],"pipeline,":[65],"named":[66],"TSR-DSAW:":[67],"TSR":[68],"via":[69],"Deep":[70],"Spatial":[71],"Association":[72],"Words,":[74],"which":[75,118],"outputs":[76],"digital":[78],"representation":[79],"structured":[86],"format":[87],"such":[88,148,191],"as":[89,95,149,192],"HTML.":[90],"Given":[91],"input,":[96],"proposed":[98],"method":[99],"begins":[100],"with":[101],"detection":[103],"all":[105],"words":[107],"using":[112,126],"text-detection":[114],"like":[116],"CRAFT":[117],"followed":[120],"generation":[123],"word-pairs":[125,130],"dynamic":[127],"programming.":[128],"These":[129],"are":[131],"highlighted":[132],"individual":[134],"images":[135],"subsequently,":[137],"fed":[138],"into":[139],"DenseNet-121":[141],"classifier":[142,161],"trained":[143],"same-row,":[150],"same-column,":[151],"same-cell":[152],"or":[153],"none.":[154],"Finally,":[155],"perform":[157],"post-processing":[158],"output":[162],"generate":[164],"HTML":[169],"format.":[170],"evaluate":[172],"our":[173],"TSR-DSAW":[174],"pipeline":[175],"two":[177],"public":[178],"table-image":[179],"datasets":[180],"-PubTabNet":[181],"ICDAR":[183],"2013,":[184],"demonstrate":[186],"improvement":[187],"over":[188],"previous":[189],"TableNet":[193],"DeepDeSRT.":[195]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":2}],"updated_date":"2026-08-04T08:18:43.703281","created_date":"2025-10-10T00:00:00"}