{"id":"https://openalex.org/W4256120941","doi":"https://doi.org/10.1109/jcdl.2017.7991587","title":"Retrieving and Combining Repeated Passages to Improve OCR","display_name":"Retrieving and Combining Repeated Passages to Improve OCR","publication_year":2017,"publication_date":"2017-06-01","ids":{"openalex":"https://openalex.org/W4256120941","doi":"https://doi.org/10.1109/jcdl.2017.7991587"},"language":"en","primary_location":{"id":"doi:10.1109/jcdl.2017.7991587","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jcdl.2017.7991587","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 ACM/IEEE Joint Conference on Digital Libraries (JCDL)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5090085269","display_name":"Shaobin Xu","orcid":null},"institutions":[{"id":"https://openalex.org/I12912129","display_name":"Northeastern University","ror":"https://ror.org/04t5xt781","country_code":"US","type":"education","lineage":["https://openalex.org/I12912129"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Shaobin Xu","raw_affiliation_strings":["College of Computer and Information Science, Northeastern University, Boston, MA"],"affiliations":[{"raw_affiliation_string":"College of Computer and Information Science, Northeastern University, Boston, MA","institution_ids":["https://openalex.org/I12912129"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101722493","display_name":"David A. Smith","orcid":"https://orcid.org/0000-0002-6636-6940"},"institutions":[{"id":"https://openalex.org/I12912129","display_name":"Northeastern University","ror":"https://ror.org/04t5xt781","country_code":"US","type":"education","lineage":["https://openalex.org/I12912129"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"David Smith","raw_affiliation_strings":["College of Computer and Information Science, Northeastern University, Boston, MA"],"affiliations":[{"raw_affiliation_string":"College of Computer and Information Science, Northeastern University, Boston, MA","institution_ids":["https://openalex.org/I12912129"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5090085269"],"corresponding_institution_ids":["https://openalex.org/I12912129"],"apc_list":null,"apc_paid":null,"fwci":0.4551,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.74539931,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"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/T10601","display_name":"Handwritten Text Recognition Techniques","score":0.9998000264167786,"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":0.9998000264167786,"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/T13523","display_name":"Mathematics, Computing, and Information Processing","score":0.994700014591217,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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":0.9832000136375427,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8042633533477783},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.7107400894165039},{"id":"https://openalex.org/keywords/optical-character-recognition","display_name":"Optical character recognition","score":0.7095266580581665},{"id":"https://openalex.org/keywords/word-error-rate","display_name":"Word error rate","score":0.6896190643310547},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.6614583134651184},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.6347777247428894},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.5657922029495239},{"id":"https://openalex.org/keywords/newspaper","display_name":"Newspaper","score":0.5525781512260437},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.5482363104820251},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5395509600639343},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5202573537826538},{"id":"https://openalex.org/keywords/character","display_name":"Character (mathematics)","score":0.477762371301651},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4165317118167877},{"id":"https://openalex.org/keywords/state","display_name":"State (computer science)","score":0.41425031423568726},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.23786744475364685},{"id":"https://openalex.org/keywords/linguistics","display_name":"Linguistics","score":0.1351022720336914},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.08479467034339905}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8042633533477783},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.7107400894165039},{"id":"https://openalex.org/C546480517","wikidata":"https://www.wikidata.org/wiki/Q167555","display_name":"Optical character recognition","level":3,"score":0.7095266580581665},{"id":"https://openalex.org/C40969351","wikidata":"https://www.wikidata.org/wiki/Q3516228","display_name":"Word error rate","level":2,"score":0.6896190643310547},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.6614583134651184},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.6347777247428894},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.5657922029495239},{"id":"https://openalex.org/C201280247","wikidata":"https://www.wikidata.org/wiki/Q11032","display_name":"Newspaper","level":2,"score":0.5525781512260437},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5482363104820251},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5395509600639343},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5202573537826538},{"id":"https://openalex.org/C2780861071","wikidata":"https://www.wikidata.org/wiki/Q1062934","display_name":"Character (mathematics)","level":2,"score":0.477762371301651},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4165317118167877},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.41425031423568726},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.23786744475364685},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.1351022720336914},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.08479467034339905},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C112698675","wikidata":"https://www.wikidata.org/wiki/Q37038","display_name":"Advertising","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/jcdl.2017.7991587","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jcdl.2017.7991587","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 ACM/IEEE Joint Conference on Digital Libraries (JCDL)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320332161","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W1634257591","https://openalex.org/W1654587182","https://openalex.org/W1814244002","https://openalex.org/W1968290777","https://openalex.org/W2018840051","https://openalex.org/W2024348326","https://openalex.org/W2038329042","https://openalex.org/W2045566081","https://openalex.org/W2057960659","https://openalex.org/W2085922539","https://openalex.org/W2098967804","https://openalex.org/W2148578434","https://openalex.org/W4243754476","https://openalex.org/W6636923708","https://openalex.org/W6638395436","https://openalex.org/W6655087157","https://openalex.org/W6674896135"],"related_works":["https://openalex.org/W2013767392","https://openalex.org/W3190162935","https://openalex.org/W1566315437","https://openalex.org/W2892857623","https://openalex.org/W2595338207","https://openalex.org/W2151348424","https://openalex.org/W2594897229","https://openalex.org/W1973075426","https://openalex.org/W4290708361","https://openalex.org/W4221142855"],"abstract_inverted_index":{"We":[0],"present":[1],"a":[2,30,86,101,111,122,150,157],"novel":[3],"approach":[4,34],"to":[5,81,163,179],"improve":[6],"the":[7,48,55,59,62,71,83,94,172,192],"output":[8,22,49,60],"of":[9,50,61,70,85,90,106,113,119,128,171],"optical":[10],"character":[11],"recognition":[12],"(OCR)":[13],"systems":[14,53],"by":[15],"first":[16],"detecting":[17],"duplicate":[18],"passages":[19,137],"in":[20,199],"their":[21],"and":[23,38,145],"then":[24],"performing":[25],"consensus":[26,146],"decoding":[27,147],"combined":[28,41,78,148],"with":[29,79,149,177],"language":[31,151,188],"model.":[32],"This":[33],"is":[35,196],"orthogonal":[36],"to,":[37],"may":[39,75],"be":[40,77],"with,":[42],"previously":[43],"proposed":[44],"methods":[45,80],"for":[46,100],"combining":[47],"different":[51],"OCR":[52,64,91],"on":[54,66,136],"same":[56,63,72],"image":[57],"or":[58,140],"system":[65],"differently":[67],"processed":[68],"images":[69],"text.":[73],"It":[74],"also":[76],"estimate":[82],"parameters":[84],"noisy":[87],"channel":[88],"model":[89,152,189],"errors.":[92],"Additionally,":[93],"current":[95],"method":[96],"generalizes":[97],"previous":[98],"proposals":[99],"simple":[102],"majority-":[103],"vote":[104,132,186],"combination":[105],"known":[107],"duplicated":[108],"texts.":[109],"On":[110],"corpus":[112],"historical":[114],"newspapers,":[115],"an":[116],"annotated":[117],"set":[118],"clusters":[120,176],"has":[121],"baseline":[123],"word":[124],"error":[125],"rate":[126],"(WER)":[127],"33%.":[129],"A":[130],"majority":[131,185],"procedure":[133],"reaches":[134],"23%":[135],"where":[138],"one":[139],"more":[141],"duplicates":[142],"were":[143,161],"found,":[144],"achieves":[153],"18%":[154],"WER.":[155],"In":[156],"separate":[158],"experiment,":[159],"newspapers":[160],"aligned":[162],"very":[164],"widely":[165],"reprinted":[166],"texts":[167],"such":[168],"as":[169],"State":[170],"Union":[173],"speeches,":[174],"producing":[175],"up":[178],"58":[180],"witnesses.":[181],"Beyond":[182],"20":[183],"witnesses,":[184],"outperforms":[187],"rescoring,":[190],"though":[191],"gap":[193],"between":[194],"them":[195],"much":[197],"less":[198],"this":[200],"experiment.":[201]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}