{"id":"https://openalex.org/W1757231607","doi":"https://doi.org/10.1109/icdar.1993.395667","title":"Recognition enhancement by linear tournament verification","display_name":"Recognition enhancement by linear tournament verification","publication_year":2002,"publication_date":"2002-12-30","ids":{"openalex":"https://openalex.org/W1757231607","doi":"https://doi.org/10.1109/icdar.1993.395667","mag":"1757231607"},"language":"en","primary_location":{"id":"doi:10.1109/icdar.1993.395667","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icdar.1993.395667","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR '93)","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/A5043196827","display_name":"H. Tokahashi","orcid":null},"institutions":[{"id":"https://openalex.org/I1341412227","display_name":"IBM (United States)","ror":"https://ror.org/05hh8d621","country_code":"US","type":"company","lineage":["https://openalex.org/I1341412227"]},{"id":"https://openalex.org/I4210085935","display_name":"IBM Research - Almaden","ror":"https://ror.org/005w8dd04","country_code":"US","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210085935","https://openalex.org/I4210114115"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"H. Tokahashi","raw_affiliation_strings":["IBM Almaden Res. Center, San Jose, CA, USA","IBM Almaden Research Center, San Jose, CA USA"],"affiliations":[{"raw_affiliation_string":"IBM Almaden Res. Center, San Jose, CA, USA","institution_ids":["https://openalex.org/I4210085935"]},{"raw_affiliation_string":"IBM Almaden Research Center, San Jose, CA USA","institution_ids":["https://openalex.org/I1341412227"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5005571490","display_name":"Thomas D. Griffin","orcid":"https://orcid.org/0000-0001-5214-8507"},"institutions":[{"id":"https://openalex.org/I1341412227","display_name":"IBM (United States)","ror":"https://ror.org/05hh8d621","country_code":"US","type":"company","lineage":["https://openalex.org/I1341412227"]},{"id":"https://openalex.org/I4210085935","display_name":"IBM Research - Almaden","ror":"https://ror.org/005w8dd04","country_code":"US","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210085935","https://openalex.org/I4210114115"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"T.D. Griffin","raw_affiliation_strings":["IBM Almaden Research Center, San Jose, CA, USA","IBM Almaden Research Center, San Jose, CA USA"],"affiliations":[{"raw_affiliation_string":"IBM Almaden Research Center, San Jose, CA, USA","institution_ids":["https://openalex.org/I4210085935"]},{"raw_affiliation_string":"IBM Almaden Research Center, San Jose, CA USA","institution_ids":["https://openalex.org/I1341412227"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5043196827"],"corresponding_institution_ids":["https://openalex.org/I1341412227","https://openalex.org/I4210085935"],"apc_list":null,"apc_paid":null,"fwci":3.47,"has_fulltext":false,"cited_by_count":24,"citation_normalized_percentile":{"value":0.93503044,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"585","last_page":"588"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10601","display_name":"Handwritten Text Recognition Techniques","score":0.9465000033378601,"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":0.9465000033378601,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9240000247955322,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9239000082015991,"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/nist","display_name":"NIST","score":0.8464797139167786},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7392768859863281},{"id":"https://openalex.org/keywords/base","display_name":"Base (topology)","score":0.6281291842460632},{"id":"https://openalex.org/keywords/voting","display_name":"Voting","score":0.6247718334197998},{"id":"https://openalex.org/keywords/alphabet","display_name":"Alphabet","score":0.5808629393577576},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5305016040802002},{"id":"https://openalex.org/keywords/scheme","display_name":"Scheme (mathematics)","score":0.5111871361732483},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5065740346908569},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.49410369992256165},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.44349780678749084},{"id":"https://openalex.org/keywords/layer","display_name":"Layer (electronics)","score":0.4190458357334137},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3701438009738922},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.36887383460998535},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.34445488452911377},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3419341444969177},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.26983416080474854},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.13303130865097046},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.10073146224021912}],"concepts":[{"id":"https://openalex.org/C111219384","wikidata":"https://www.wikidata.org/wiki/Q6954384","display_name":"NIST","level":2,"score":0.8464797139167786},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7392768859863281},{"id":"https://openalex.org/C42058472","wikidata":"https://www.wikidata.org/wiki/Q810214","display_name":"Base (topology)","level":2,"score":0.6281291842460632},{"id":"https://openalex.org/C520049643","wikidata":"https://www.wikidata.org/wiki/Q189760","display_name":"Voting","level":3,"score":0.6247718334197998},{"id":"https://openalex.org/C112876837","wikidata":"https://www.wikidata.org/wiki/Q837518","display_name":"Alphabet","level":2,"score":0.5808629393577576},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5305016040802002},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.5111871361732483},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5065740346908569},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49410369992256165},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.44349780678749084},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.4190458357334137},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3701438009738922},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36887383460998535},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.34445488452911377},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3419341444969177},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.26983416080474854},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.13303130865097046},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.10073146224021912},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"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/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icdar.1993.395667","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icdar.1993.395667","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR '93)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":7,"referenced_works":["https://openalex.org/W353738041","https://openalex.org/W2053380612","https://openalex.org/W2164568552","https://openalex.org/W2497990927","https://openalex.org/W4253020087","https://openalex.org/W4285719527","https://openalex.org/W6724147632"],"related_works":["https://openalex.org/W2158491338","https://openalex.org/W2807901368","https://openalex.org/W2133733652","https://openalex.org/W2072658171","https://openalex.org/W2606392311","https://openalex.org/W2320042380","https://openalex.org/W4385956668","https://openalex.org/W2900895161","https://openalex.org/W4380838366","https://openalex.org/W2355283996"],"abstract_inverted_index":{"The":[0],"recent":[1],"significant":[2],"enhancement":[3,39],"of":[4,40,49,59,83,98],"OCR":[5,62],"systems":[6],"recognition":[7,41,123],"rates":[8],"has":[9],"been":[10],"driven":[11],"mainly":[12],"by":[13,19],"combining":[14],"different":[15],"feature":[16],"sets":[17],"or":[18],"adopting":[20],"a":[21,46,50,68,132],"voting":[22],"scheme":[23],"using":[24,74],"multiple":[25],"independent":[26],"algorithms.":[27],"Voting":[28],"is":[29,54,72,129,143],"effective":[30],"but":[31],"computationally":[32],"expensive.":[33],"A":[34],"general":[35],"framework":[36],"for":[37,92,125],"economical":[38],"rate":[42,124],"that":[43],"focuses":[44],"on":[45],"critical":[47],"reordering":[48],"few":[51],"top":[52,85],"candidates":[53],"described.":[55],"After":[56],"the":[57,60,81,84,93,104,107,113,117,122,126,136,141,145],"execution":[58],"base":[61,114],"algorithm":[63],"(a":[64],"three-layer":[65],"neural":[66],"network),":[67],"linear":[69],"tournament":[70],"verification":[71],"executed":[73],"one-to-one":[75,88],"small":[76],"network":[77],"verifiers":[78,89],"to":[79],"improve":[80],"ordering":[82],"candidates.":[86],"Thirty-four":[87],"were":[90],"developed":[91],"uppercase":[94,119],"English":[95],"alphabet.":[96],"Fourteen":[97],"these":[99],"use":[100,106],"special":[101],"features;":[102],"however,":[103],"rest":[105],"same":[108],"features":[109],"as":[110],"those":[111],"in":[112,147],"algorithm.":[115],"On":[116],"NIST":[118],"data":[120],"set,":[121],"new":[127],"system":[128,137],"95.8%,":[130],"showing":[131],"1.2%":[133],"improvement":[134,142],"over":[135],"without":[138],"verification.":[139],"Although":[140],"modest,":[144],"costs":[146],"both":[148],"efficiency":[149],"and":[150],"development":[151],"effort":[152],"are":[153],"small.<":[154],"<ETX":[155],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[156],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">&gt;</ETX>":[157]},"counts_by_year":[{"year":2017,"cited_by_count":1},{"year":2014,"cited_by_count":1},{"year":2012,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
