{"id":"https://openalex.org/W3161195344","doi":"https://doi.org/10.1109/icpr48806.2021.9413166","title":"MEAN: Multi - Element Attention Network for Scene Text Recognition","display_name":"MEAN: Multi - Element Attention Network for Scene Text Recognition","publication_year":2021,"publication_date":"2021-01-10","ids":{"openalex":"https://openalex.org/W3161195344","doi":"https://doi.org/10.1109/icpr48806.2021.9413166","mag":"3161195344"},"language":"en","primary_location":{"id":"doi:10.1109/icpr48806.2021.9413166","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr48806.2021.9413166","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 25th International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5061250546","display_name":"Ruijie Yan","orcid":"https://orcid.org/0000-0002-2743-8470"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ruijie Yan","raw_affiliation_strings":["Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076442506","display_name":"Liangrui Peng","orcid":"https://orcid.org/0000-0001-7793-1039"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liangrui Peng","raw_affiliation_strings":["Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065205445","display_name":"Shanyu Xiao","orcid":"https://orcid.org/0000-0003-3025-6157"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shanyu Xiao","raw_affiliation_strings":["Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059777846","display_name":"Gang Yao","orcid":"https://orcid.org/0000-0001-9311-0337"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Gang Yao","raw_affiliation_strings":["Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5005935646","display_name":"Jaesik Min","orcid":"https://orcid.org/0000-0002-0007-0637"},"institutions":[{"id":"https://openalex.org/I197312522","display_name":"Hyundai Motor Group (South Korea)","ror":"https://ror.org/05kxbz959","country_code":"KR","type":"company","lineage":["https://openalex.org/I197312522"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jaesik Min","raw_affiliation_strings":["Hyundai Motor Group AIRS Company, Seoul, Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hyundai Motor Group AIRS Company, Seoul, Korea","institution_ids":["https://openalex.org/I197312522"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10601","display_name":"Handwritten Text Recognition Techniques","score":1.0,"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":1.0,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9909999966621399,"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/T12707","display_name":"Vehicle License Plate Recognition","score":0.9853000044822693,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7540441751480103},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6589862704277039},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6324244737625122},{"id":"https://openalex.org/keywords/adjacency-list","display_name":"Adjacency list","score":0.578682541847229},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.569196343421936},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.566565215587616},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.5621731877326965},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5088868141174316},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.43019410967826843},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.4169466495513916},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.367997944355011},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.14864131808280945},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.13679158687591553}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7540441751480103},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6589862704277039},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6324244737625122},{"id":"https://openalex.org/C110484373","wikidata":"https://www.wikidata.org/wiki/Q264398","display_name":"Adjacency list","level":2,"score":0.578682541847229},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.569196343421936},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.566565215587616},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.5621731877326965},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5088868141174316},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.43019410967826843},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.4169466495513916},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.367997944355011},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.14864131808280945},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.13679158687591553},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","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/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"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/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icpr48806.2021.9413166","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr48806.2021.9413166","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 25th International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6800000071525574,"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":65,"referenced_works":["https://openalex.org/W6908809","https://openalex.org/W1491389626","https://openalex.org/W1901129140","https://openalex.org/W1971822075","https://openalex.org/W1981283549","https://openalex.org/W1984279162","https://openalex.org/W1998042868","https://openalex.org/W2008806374","https://openalex.org/W2066380143","https://openalex.org/W2099247484","https://openalex.org/W2127141656","https://openalex.org/W2144554289","https://openalex.org/W2146835493","https://openalex.org/W2194187530","https://openalex.org/W2294053032","https://openalex.org/W2343052201","https://openalex.org/W2519887557","https://openalex.org/W2593572697","https://openalex.org/W2750938222","https://openalex.org/W2752225195","https://openalex.org/W2766224267","https://openalex.org/W2788069964","https://openalex.org/W2791490164","https://openalex.org/W2795619303","https://openalex.org/W2809273748","https://openalex.org/W2810983211","https://openalex.org/W2875814315","https://openalex.org/W2904498920","https://openalex.org/W2905224888","https://openalex.org/W2952285877","https://openalex.org/W2955425717","https://openalex.org/W2963233387","https://openalex.org/W2963319519","https://openalex.org/W2963327605","https://openalex.org/W2963403868","https://openalex.org/W2963517393","https://openalex.org/W2963526661","https://openalex.org/W2963712589","https://openalex.org/W2963918968","https://openalex.org/W2964015378","https://openalex.org/W2964065044","https://openalex.org/W2965066169","https://openalex.org/W2970910956","https://openalex.org/W2970971581","https://openalex.org/W2979371747","https://openalex.org/W2998382406","https://openalex.org/W3003221725","https://openalex.org/W3004846386","https://openalex.org/W3005436539","https://openalex.org/W3035449864","https://openalex.org/W3103867398","https://openalex.org/W3106271744","https://openalex.org/W3125900910","https://openalex.org/W4295312788","https://openalex.org/W4297749299","https://openalex.org/W4385245566","https://openalex.org/W6639824700","https://openalex.org/W6649973027","https://openalex.org/W6681875759","https://openalex.org/W6734085379","https://openalex.org/W6739901393","https://openalex.org/W6744310024","https://openalex.org/W6748446516","https://openalex.org/W6757161006","https://openalex.org/W6766978945"],"related_works":["https://openalex.org/W2611163850","https://openalex.org/W1845849701","https://openalex.org/W3015250136","https://openalex.org/W1980812295","https://openalex.org/W2044218713","https://openalex.org/W2625268413","https://openalex.org/W4390516098","https://openalex.org/W2962989220","https://openalex.org/W2379234996","https://openalex.org/W2900798793"],"abstract_inverted_index":{"Scene":[0],"text":[1,20,58,130,178,202,214],"recognition":[2],"is":[3,40,63,111,135,154],"a":[4,34,56,64,115,126,145,194],"challenging":[5],"problem":[6],"due":[7],"to":[8,42,48,92,137],"the":[9,28,78,103,142,151,157,160,198],"wide":[10],"variances":[11],"in":[12,22,51,72],"contents,":[13],"styles,":[14],"orientations,":[15],"and":[16,84,98,125,144,186,211],"image":[17],"quality":[18],"of":[19,31,67,80,87,159,197],"instances":[21],"natural":[23],"scene":[24,32,57,177,201,213],"images.":[25],"To":[26],"learn":[27],"intrinsic":[29],"representation":[30],"texts,":[33],"novel":[35],"multi-element":[36,107],"attention":[37,104,108],"(MEA)":[38],"mechanism":[39,62,124],"proposed":[41],"exploit":[43],"geometric":[44],"structures":[45],"from":[46,55,150],"local":[47],"global":[49,99],"levels":[50,100],"feature":[52,73,118,138,146,152],"maps":[53,74,139,153],"extracted":[54],"image.":[59],"The":[60,70],"MEA":[61,123],"generalized":[65],"form":[66],"self-attention":[68],"technique.":[69],"elements":[71],"are":[75,90],"taken":[76],"as":[77,156],"nodes":[79],"an":[81,120],"undirected":[82],"graph,":[83],"three":[85],"kinds":[86],"adjacency":[88],"matrices":[89],"designed":[91],"aggregate":[93],"information":[94],"at":[95],"local,":[96],"neighborhood":[97],"before":[101],"calculating":[102],"weights.":[105],"A":[106],"network":[109],"(MEAN)":[110],"implemented,":[112],"which":[113],"includes":[114],"CNN":[116],"for":[117,128],"extraction,":[119],"encoder":[121],"with":[122],"decoder":[127],"predicting":[129],"codes.":[131],"Orientational":[132],"positional":[133],"encoding":[134],"added":[136],"output":[140],"by":[141],"CNN,":[143],"vector":[147],"sequence":[148],"transformed":[149],"used":[155],"input":[158],"encoder.":[161],"Experimental":[162],"results":[163],"show":[164],"that":[165,205],"MEAN":[166,206],"has":[167],"achieved":[168],"state-of-the-art":[169],"or":[170],"competitive":[171],"performance":[172],"on":[173,193],"seven":[174],"public":[175],"English":[176],"datasets":[179],"(IIITSk,":[180],"SVT,":[181],"IC03,":[182],"IC13,":[183],"IC15,":[184],"SVTP,":[185],"CUTE).":[187],"Further":[188],"experiments":[189],"have":[190],"been":[191],"conducted":[192],"selected":[195],"subset":[196],"RCTW":[199],"Chinese":[200],"dataset,":[203],"demonstrating":[204],"can":[207],"handle":[208],"horizontal,":[209],"vertical,":[210],"irregular":[212],"samples.":[215]},"counts_by_year":[{"year":2023,"cited_by_count":2},{"year":2021,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}