{"id":"https://openalex.org/W7166847314","doi":"https://doi.org/10.48550/arxiv.2606.31446","title":"Revising RVL-CDIP: Quantifying Errors and Test-Train Overlap","display_name":"Revising RVL-CDIP: Quantifying Errors and Test-Train Overlap","publication_year":2026,"publication_date":"2026-06-30","ids":{"openalex":"https://openalex.org/W7166847314","doi":"https://doi.org/10.48550/arxiv.2606.31446"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.31446","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.31446","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_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.31446","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5139823396","display_name":"Stefan Larson","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Larson, Stefan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103413905","display_name":"A. B. Nagy","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nagy, Attila","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139823972","display_name":"Sam Desai","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Desai, Sam","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139790676","display_name":"Cyrus Desai","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Desai, Cyrus","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139743020","display_name":"Nicole C. Lima","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lima, Nicole C.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139846290","display_name":"Yixin Yuan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yuan, Yixin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139815120","display_name":"Siddharth Betala","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Betala, Siddharth","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139784884","display_name":"Kaushal K. Prajapati","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Prajapati, Kaushal K.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5092721684","display_name":"Jamiu T. Suleiman","orcid":"https://orcid.org/0009-0009-2397-7109"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Suleiman, Jamiu T.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139797974","display_name":"Sharad Duwal","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Duwal, Sharad","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5030030910","display_name":"Kevin Leach","orcid":"https://orcid.org/0000-0002-4001-3442"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Leach, Kevin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"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":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.5152000188827515,"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"}},"topics":[{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.5152000188827515,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.14970000088214874,"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/T10601","display_name":"Handwritten Text Recognition Techniques","score":0.1168999969959259,"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/benchmarking","display_name":"Benchmarking","score":0.7360000014305115},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6453999876976013},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4480000138282776},{"id":"https://openalex.org/keywords/error-detection-and-correction","display_name":"Error detection and correction","score":0.3562000095844269},{"id":"https://openalex.org/keywords/percentage-point","display_name":"Percentage point","score":0.3521000146865845},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.34459999203681946}],"concepts":[{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.7360000014305115},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6453999876976013},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6317999958992004},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5548999905586243},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5040000081062317},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.48170000314712524},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4480000138282776},{"id":"https://openalex.org/C103088060","wikidata":"https://www.wikidata.org/wiki/Q1062839","display_name":"Error detection and correction","level":2,"score":0.3562000095844269},{"id":"https://openalex.org/C44648626","wikidata":"https://www.wikidata.org/wiki/Q1049848","display_name":"Percentage point","level":2,"score":0.3521000146865845},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.34459999203681946},{"id":"https://openalex.org/C3018824978","wikidata":"https://www.wikidata.org/wiki/Q2894891","display_name":"Error analysis","level":2,"score":0.32690000534057617},{"id":"https://openalex.org/C167085575","wikidata":"https://www.wikidata.org/wiki/Q6803654","display_name":"Mean squared prediction error","level":2,"score":0.3249000012874603},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.29190000891685486},{"id":"https://openalex.org/C21080849","wikidata":"https://www.wikidata.org/wiki/Q13611879","display_name":"Data point","level":2,"score":0.26899999380111694},{"id":"https://openalex.org/C19619285","wikidata":"https://www.wikidata.org/wiki/Q196372","display_name":"Observational error","level":2,"score":0.2581999897956848},{"id":"https://openalex.org/C40969351","wikidata":"https://www.wikidata.org/wiki/Q3516228","display_name":"Word error rate","level":2,"score":0.25040000677108765}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.31446","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.31446","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_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.31446","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.31446","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_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"RVL-CDIP":[0,61,77,83],"is":[1],"a":[2],"popular":[3],"dataset":[4,11],"for":[5],"benchmarking":[6],"document":[7,71],"classifiers.":[8],"However,":[9],"the":[10,86],"contains":[12,88],"ample":[13],"amounts":[14,22],"of":[15,23,27,60,82,141],"label":[16,47,63,90],"errors":[17,104],"as":[18,20,149,151],"well":[19],"non-trivial":[21],"test-train":[24,54,66,95],"overlap,":[25],"both":[26],"which":[28],"may":[29],"impact":[30],"model":[31],"performance":[32,73],"metrics.":[33],"In":[34],"this":[35],"paper,":[36],"we":[37],"address":[38],"these":[39,75],"two":[40],"problems":[41],"by":[42],"(1)":[43],"finding":[44,125],"and":[45,49,52,65,69,92,147],"fixing":[46],"errors,":[48],"(2)":[50],"detecting":[51],"addressing":[53],"overlap.":[55],"We":[56,116],"produce":[57],"several":[58],"variations":[59],"with":[62,135],"error":[64,91],"overlap":[67],"fixes,":[68],"benchmark":[70],"classification":[72,101],"on":[74,120,128],"new":[76],"variations.":[78],"Our":[79],"rigorous":[80],"analysis":[81],"finds":[84],"that":[85,126],"corpus":[87],"12\\%":[89],"approximately":[93],"35%":[94],"duplication.":[96],"Remediation":[97],"sees":[98,108],"improvements":[99,148],"in":[100,110,145],"accuracy":[102,111,146],"when":[103,112],"are":[105,114],"removed,":[106],"but":[107],"decreases":[109],"duplicates":[113],"removed.":[115],"additionally":[117],"evaluate":[118],"models":[119,137],"RVL-CDIP-N,":[121],"an":[122,139],"out-of-distribution":[123],"benchmark,":[124],"training":[127],"error-corrected":[129],"data":[130],"substantially":[131],"improves":[132],"OOD":[133],"generalization,":[134],"supervised":[136],"gaining":[138],"average":[140],"8.1":[142],"percentage":[143,153],"points":[144],"large":[150],"14":[152],"points.":[154]},"counts_by_year":[],"updated_date":"2026-07-02T06:18:51.028212","created_date":"2026-07-02T00:00:00"}