{"id":"https://openalex.org/W4290802733","doi":"https://doi.org/10.48550/arxiv.2208.04921","title":"TSRFormer: Table Structure Recognition with Transformers","display_name":"TSRFormer: Table Structure Recognition with Transformers","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4290802733","doi":"https://doi.org/10.48550/arxiv.2208.04921"},"language":"en","primary_location":{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2208.04921","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_indexed_in_scopus":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":["Cornell University"],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false},"type":"preprint","type_crossref":"posted-content","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/abs/2208.04921","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101062259","display_name":"Weihong Lin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lin, Weihong","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100625498","display_name":"Zheng Sun","orcid":"https://orcid.org/0000-0003-0028-9441"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sun, Zheng","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004114734","display_name":"Chixiang Ma","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ma, Chixiang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100621995","display_name":"Mingze Li","orcid":"https://orcid.org/0000-0001-7721-0768"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Mingze","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100388745","display_name":"Jiawei Wang","orcid":"https://orcid.org/0000-0001-5037-4658"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Jiawei","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100699930","display_name":"Lei Sun","orcid":"https://orcid.org/0000-0001-9960-205X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sun, Lei","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5039662070","display_name":"Qiang Huo","orcid":"https://orcid.org/0000-0003-2464-6482"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huo, Qiang","raw_affiliation_strings":[],"affiliations":[]}],"institution_assertions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.820962,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":81,"max":84},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T10601","display_name":"Handwritten Text Recognition Techniques","score":0.9995,"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":0.9995,"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/T12357","display_name":"Digital Media Forensic Detection","score":0.9951,"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/T14319","display_name":"Currency Recognition and Detection","score":0.994,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness","score":0.5653224},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5415249}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6633148},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5653224},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5416608},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5415249},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5171089},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.47950727},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4598452},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4311098},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3785513},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2208.04921","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_indexed_in_scopus":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":["Cornell University"],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false},{"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2208.04921","pdf_url":"http://arxiv.org/pdf/2208.04921","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_indexed_in_scopus":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":["Cornell University"],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false},{"is_oa":false,"landing_page_url":"https://api.datacite.org/dois/10.48550/arxiv.2208.04921","pdf_url":null,"source":{"id":"https://openalex.org/S4393179698","display_name":"DataCite API","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_indexed_in_scopus":false,"is_core":false,"host_organization":"https://openalex.org/I4210145204","host_organization_name":"DataCite","host_organization_lineage":["https://openalex.org/I4210145204"],"host_organization_lineage_names":["DataCite"],"type":"metadata"},"license":null,"license_id":null,"version":null}],"best_oa_location":{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2208.04921","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_indexed_in_scopus":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":["Cornell University"],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false},"sustainable_development_goals":[],"grants":[],"datasets":[],"versions":[],"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W972276598","https://openalex.org/W4321353415","https://openalex.org/W4246352526","https://openalex.org/W2745001401","https://openalex.org/W2378211422","https://openalex.org/W2130974462","https://openalex.org/W2087343574","https://openalex.org/W2086519370","https://openalex.org/W2028665553","https://openalex.org/W1996690921"],"abstract_inverted_index":{"We":[0],"present":[1],"a":[2,36,48,112,133,197],"new":[3,49,104,149],"table":[4,24,31,66],"structure":[5],"recognition":[6],"(TSR)":[7],"approach,":[8,55],"called":[9],"TSRFormer,":[10],"to":[11,61,94,108,143,174],"robustly":[12],"recognizing":[13],"the":[14,71,80,96,169],"structures":[15],"of":[16,41,100,171],"complex":[17,177],"tables":[18,175],"with":[19,125,176],"geometrical":[20],"distortions":[21],"from":[22,65,111],"various":[23],"images.":[25],"Unlike":[26],"previous":[27],"methods,":[28],"we":[29,85,166],"formulate":[30],"separation":[32,63,81,130],"line":[33,37,82,131],"prediction":[34,54,83],"as":[35,188,190],"regression":[38],"problem":[39,45],"instead":[40],"an":[42],"image":[43],"segmentation":[44],"and":[46,77,163],"propose":[47,86],"two-stage":[50,72],"DETR":[51,73],"based":[52,137],"separator":[53],"dubbed":[56],"\\textbf{Sep}arator":[57],"\\textbf{RE}gression":[58],"\\textbf{TR}ansformer":[59],"(SepRETR),":[60],"predict":[62],"lines":[64],"images":[67],"directly.":[68],"To":[69],"make":[70],"framework":[74],"work":[75],"efficiently":[76],"effectively":[78],"for":[79],"task,":[84],"two":[87],"improvements:":[88],"1)":[89],"A":[90,103],"prior-enhanced":[91],"matching":[92],"strategy":[93],"solve":[95],"slow":[97],"convergence":[98],"issue":[99],"DETR;":[101],"2)":[102],"cross":[105],"attention":[106],"module":[107,140],"sample":[109],"features":[110],"high-resolution":[113],"convolutional":[114],"feature":[115],"map":[116],"directly":[117],"so":[118],"that":[119],"high":[120],"localization":[121],"accuracy":[122],"is":[123,141],"achieved":[124],"low":[126],"computational":[127],"cost.":[128],"After":[129],"prediction,":[132],"simple":[134],"relation":[135],"network":[136],"cell":[138],"merging":[139],"used":[142],"recover":[144],"spanning":[145,186],"cells.":[146],"With":[147],"these":[148],"techniques,":[150],"our":[151,172],"TSRFormer":[152],"achieves":[153],"state-of-the-art":[154],"performance":[155],"on":[156,196],"several":[157],"benchmark":[158],"datasets,":[159],"including":[160],"SciTSR,":[161],"PubTabNet":[162],"WTW.":[164],"Furthermore,":[165],"have":[167],"validated":[168],"robustness":[170],"approach":[173],"structures,":[178],"borderless":[179],"cells,":[180],"large":[181],"blank":[182],"spaces,":[183],"empty":[184],"or":[185,192],"cells":[187],"well":[189],"distorted":[191],"even":[193],"curved":[194],"shapes":[195],"more":[198],"challenging":[199],"real-world":[200],"in-house":[201],"dataset.":[202]},"abstract_inverted_index_v3":null,"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W4290802733","counts_by_year":[{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":2}],"updated_date":"2025-04-21T13:30:27.533606","created_date":"2022-08-12"}