{"id":"https://openalex.org/W3026638717","doi":"https://doi.org/10.1109/wacvw50321.2020.9096928","title":"Memory-Efficient Models for Scene Text Recognition via Neural Architecture Search","display_name":"Memory-Efficient Models for Scene Text Recognition via Neural Architecture Search","publication_year":2020,"publication_date":"2020-03-01","ids":{"openalex":"https://openalex.org/W3026638717","doi":"https://doi.org/10.1109/wacvw50321.2020.9096928","mag":"3026638717"},"language":"en","primary_location":{"id":"doi:10.1109/wacvw50321.2020.9096928","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacvw50321.2020.9096928","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE Winter Applications of Computer Vision Workshops (WACVW)","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/A5032540107","display_name":"SeulGi Hong","orcid":"https://orcid.org/0000-0002-2756-7053"},"institutions":[{"id":"https://openalex.org/I157485424","display_name":"Korea Advanced Institute of Science and Technology","ror":"https://ror.org/05apxxy63","country_code":"KR","type":"education","lineage":["https://openalex.org/I157485424"]},{"id":"https://openalex.org/I4210099236","display_name":"Kootenay Association for Science & Technology","ror":"https://ror.org/011pv9p44","country_code":"CA","type":"nonprofit","lineage":["https://openalex.org/I4210099236"]}],"countries":["CA","KR"],"is_corresponding":true,"raw_author_name":"SeulGi Hong","raw_affiliation_strings":["hutom, KAIST"],"affiliations":[{"raw_affiliation_string":"hutom, KAIST","institution_ids":["https://openalex.org/I4210099236","https://openalex.org/I157485424"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100358136","display_name":"Donghyun Kim","orcid":"https://orcid.org/0000-0001-9033-5231"},"institutions":[{"id":"https://openalex.org/I60922564","display_name":"Naver (South Korea)","ror":"https://ror.org/04nzrnx83","country_code":"KR","type":"company","lineage":["https://openalex.org/I60922564"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"DongHyun Kim","raw_affiliation_strings":["Clova AI Research, NAVER Corp"],"affiliations":[{"raw_affiliation_string":"Clova AI Research, NAVER Corp","institution_ids":["https://openalex.org/I60922564"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5000459808","display_name":"Min-Kook Choi","orcid":"https://orcid.org/0000-0001-7610-631X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Min-Kook Choi","raw_affiliation_strings":["hutom"],"affiliations":[{"raw_affiliation_string":"hutom","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5032540107"],"corresponding_institution_ids":["https://openalex.org/I157485424","https://openalex.org/I4210099236"],"apc_list":null,"apc_paid":null,"fwci":0.5862,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.68325267,"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":"183","last_page":"191"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10601","display_name":"Handwritten Text Recognition Techniques","score":0.9998999834060669,"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":0.9998999834060669,"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/T14339","display_name":"Image Processing and 3D Reconstruction","score":0.9980999827384949,"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":0.9944000244140625,"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/computer-science","display_name":"Computer science","score":0.7847826480865479},{"id":"https://openalex.org/keywords/architecture","display_name":"Architecture","score":0.630508542060852},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5156687498092651},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.4084956645965576},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.36709102988243103},{"id":"https://openalex.org/keywords/computer-architecture","display_name":"Computer architecture","score":0.3451559245586395},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.3269713819026947}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7847826480865479},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.630508542060852},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5156687498092651},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4084956645965576},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.36709102988243103},{"id":"https://openalex.org/C118524514","wikidata":"https://www.wikidata.org/wiki/Q173212","display_name":"Computer architecture","level":1,"score":0.3451559245586395},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.3269713819026947},{"id":"https://openalex.org/C153349607","wikidata":"https://www.wikidata.org/wiki/Q36649","display_name":"Visual arts","level":1,"score":0.0},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/wacvw50321.2020.9096928","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacvw50321.2020.9096928","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE Winter Applications of Computer Vision Workshops (WACVW)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":59,"referenced_works":["https://openalex.org/W1491389626","https://openalex.org/W1686810756","https://openalex.org/W1971822075","https://openalex.org/W1981283549","https://openalex.org/W1998042868","https://openalex.org/W2008806374","https://openalex.org/W2112796928","https://openalex.org/W2127141656","https://openalex.org/W2142069714","https://openalex.org/W2144554289","https://openalex.org/W2146835493","https://openalex.org/W2194187530","https://openalex.org/W2194775991","https://openalex.org/W2294053032","https://openalex.org/W2298368322","https://openalex.org/W2335728318","https://openalex.org/W2343052201","https://openalex.org/W2553303224","https://openalex.org/W2593572697","https://openalex.org/W2748513770","https://openalex.org/W2750938222","https://openalex.org/W2752225195","https://openalex.org/W2795619303","https://openalex.org/W2803311163","https://openalex.org/W2809273748","https://openalex.org/W2810075754","https://openalex.org/W2810983211","https://openalex.org/W2910554758","https://openalex.org/W2927058921","https://openalex.org/W2927980542","https://openalex.org/W2936599103","https://openalex.org/W2951104886","https://openalex.org/W2952302849","https://openalex.org/W2952439627","https://openalex.org/W2962746461","https://openalex.org/W2962835968","https://openalex.org/W2963136578","https://openalex.org/W2963163009","https://openalex.org/W2963374479","https://openalex.org/W2963473542","https://openalex.org/W2963517393","https://openalex.org/W2963526661","https://openalex.org/W2963778169","https://openalex.org/W2964081807","https://openalex.org/W2964259004","https://openalex.org/W2964444661","https://openalex.org/W2982440944","https://openalex.org/W3004846386","https://openalex.org/W3106271744","https://openalex.org/W6629590909","https://openalex.org/W6637373629","https://openalex.org/W6729956949","https://openalex.org/W6743495212","https://openalex.org/W6744310024","https://openalex.org/W6745614327","https://openalex.org/W6751831502","https://openalex.org/W6752515464","https://openalex.org/W6756887525","https://openalex.org/W6766225098"],"related_works":["https://openalex.org/W2588198209","https://openalex.org/W1909006023","https://openalex.org/W4205824991","https://openalex.org/W3200723557","https://openalex.org/W4312713546","https://openalex.org/W2362195430","https://openalex.org/W2347494122","https://openalex.org/W2567983276","https://openalex.org/W2802298219","https://openalex.org/W2038503502"],"abstract_inverted_index":{"Meta-learning":[0],"techniques":[1],"based":[2,199],"on":[3,200],"neural":[4,20,32],"architecture":[5,116],"search":[6],"(NAS)":[7],"show":[8],"excellent":[9],"performance":[10,39,176],"in":[11,18,83,121,184],"the":[12,38,41,85,95,103,122,127,154,159,170,181,196],"design":[13,29,136,198],"of":[14,40,55,180,186,190],"learning":[15],"models":[16],"used":[17,146],"deep":[19],"networks.":[21],"In":[22],"particular,":[23],"when":[24,43],"NAS":[25,65,86,104,161,201],"is":[26],"applied":[27,68,126,205],"to":[28,69,101,106,178,206],"a":[30,76,81,91,107,137,143,175],"convolutional":[31],"network":[33,42],"(CNN)":[34],"for":[35,94,118],"image":[36,109],"recognition,":[37],"evaluating":[44],"public":[45],"benchmark":[46,165],"datasets":[47,166],"such":[48],"as":[49],"CIFAR10":[50],"and":[51,125,188],"ImageNet":[52],"exceeds":[53],"that":[54,140,179,195],"hand-designed":[56],"models.":[57],"Nevertheless,":[58],"there":[59],"are":[60],"very":[61],"few":[62],"cases":[63],"wherein":[64],"has":[66],"been":[67],"real-world":[70],"problems,":[71],"i.e.":[72],"recognition":[73,98,110],"problems":[74],"with":[75],"limited":[77],"dataset.":[78],"We":[79,192],"proposed":[80,114,160],"method":[82,105],"which":[84],"technique":[87],"does":[88],"not":[89],"require":[90],"proxy":[92],"task":[93],"scene":[96],"text":[97],"(STR)":[99],"framework":[100,124],"apply":[102],"new":[108,138],"field.":[111],"Therefore,":[112],"we":[113],"an":[115],"space":[117],"CNN-based":[119],"modules":[120],"STR":[123,155,164,207],"ProxylessNAS":[128],"method,":[129,162],"enabling":[130],"end-to-end":[131],"training":[132],"while":[133],"meta":[134],"learners":[135],"model":[139,156,172,183,197],"requires":[141],"only":[142],"single":[144],"commonly":[145],"GPU":[147,150],"(approximately":[148],"100":[149],"hours).":[151],"To":[152],"evaluate":[153],"obtained":[157,171],"by":[158],"seven":[163],"were":[167],"used.":[168],"Finally,":[169],"could":[173],"achieve":[174],"similar":[177],"ideal":[182],"terms":[185],"accuracy":[187],"number":[189],"parameters.":[191],"thus":[193],"confirm":[194],"can":[202],"be":[203],"effectively":[204],"scenarios.":[208]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}