{"id":"https://openalex.org/W2609829154","doi":"https://doi.org/10.1109/icpr.2016.7900259","title":"Scene text recognition with CNN classifier and WFST-based word labeling","display_name":"Scene text recognition with CNN classifier and WFST-based word labeling","publication_year":2016,"publication_date":"2016-12-01","ids":{"openalex":"https://openalex.org/W2609829154","doi":"https://doi.org/10.1109/icpr.2016.7900259","mag":"2609829154"},"language":"en","primary_location":{"id":"doi:10.1109/icpr.2016.7900259","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr.2016.7900259","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 23rd International Conference on Pattern Recognition (ICPR)","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/A5083694367","display_name":"Xinhao Liu","orcid":"https://orcid.org/0000-0002-5640-1837"},"institutions":[{"id":"https://openalex.org/I2251713219","display_name":"NTT (Japan)","ror":"https://ror.org/00berct97","country_code":"JP","type":"company","lineage":["https://openalex.org/I2251713219"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Xinhao Liu","raw_affiliation_strings":["NTT Corporation, Kanagawa, Japan 243-0198","NTT Corporation, Kanagawa, Japan"],"affiliations":[{"raw_affiliation_string":"NTT Corporation, Kanagawa, Japan 243-0198","institution_ids":["https://openalex.org/I2251713219"]},{"raw_affiliation_string":"NTT Corporation, Kanagawa, Japan","institution_ids":["https://openalex.org/I2251713219"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029349765","display_name":"Takahito Kawanishi","orcid":null},"institutions":[{"id":"https://openalex.org/I2251713219","display_name":"NTT (Japan)","ror":"https://ror.org/00berct97","country_code":"JP","type":"company","lineage":["https://openalex.org/I2251713219"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Takahito Kawanishi","raw_affiliation_strings":["NTT Corporation, Kanagawa, Japan 243-0198","NTT Corporation, Kanagawa, Japan"],"affiliations":[{"raw_affiliation_string":"NTT Corporation, Kanagawa, Japan 243-0198","institution_ids":["https://openalex.org/I2251713219"]},{"raw_affiliation_string":"NTT Corporation, Kanagawa, Japan","institution_ids":["https://openalex.org/I2251713219"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036223449","display_name":"Xiaomeng Wu","orcid":"https://orcid.org/0000-0003-2816-1781"},"institutions":[{"id":"https://openalex.org/I2251713219","display_name":"NTT (Japan)","ror":"https://ror.org/00berct97","country_code":"JP","type":"company","lineage":["https://openalex.org/I2251713219"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Xiaomeng Wu","raw_affiliation_strings":["NTT Corporation, Kanagawa, Japan 243-0198","NTT Corporation, Kanagawa, Japan"],"affiliations":[{"raw_affiliation_string":"NTT Corporation, Kanagawa, Japan 243-0198","institution_ids":["https://openalex.org/I2251713219"]},{"raw_affiliation_string":"NTT Corporation, Kanagawa, Japan","institution_ids":["https://openalex.org/I2251713219"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5061465935","display_name":"Kunio Kashino","orcid":null},"institutions":[{"id":"https://openalex.org/I2251713219","display_name":"NTT (Japan)","ror":"https://ror.org/00berct97","country_code":"JP","type":"company","lineage":["https://openalex.org/I2251713219"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kunio Kashino","raw_affiliation_strings":["NTT Corporation, Kanagawa, Japan 243-0198","NTT Corporation, Kanagawa, Japan"],"affiliations":[{"raw_affiliation_string":"NTT Corporation, Kanagawa, Japan 243-0198","institution_ids":["https://openalex.org/I2251713219"]},{"raw_affiliation_string":"NTT Corporation, Kanagawa, Japan","institution_ids":["https://openalex.org/I2251713219"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5083694367"],"corresponding_institution_ids":["https://openalex.org/I2251713219"],"apc_list":null,"apc_paid":null,"fwci":0.3388,"has_fulltext":false,"cited_by_count":16,"citation_normalized_percentile":{"value":0.6993794,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"3999","last_page":"4004"},"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9955000281333923,"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":0.9952999949455261,"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":0.815582275390625},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7426497936248779},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7192274332046509},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6617499589920044},{"id":"https://openalex.org/keywords/lexicon","display_name":"Lexicon","score":0.5926306843757629},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5805264711380005},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.4485244154930115},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.4000599682331085},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.3806000053882599},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.07959437370300293}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.815582275390625},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7426497936248779},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7192274332046509},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6617499589920044},{"id":"https://openalex.org/C2778121359","wikidata":"https://www.wikidata.org/wiki/Q8096","display_name":"Lexicon","level":2,"score":0.5926306843757629},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5805264711380005},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.4485244154930115},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.4000599682331085},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3806000053882599},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.07959437370300293},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icpr.2016.7900259","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr.2016.7900259","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 23rd International Conference on Pattern Recognition (ICPR)","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":41,"referenced_works":["https://openalex.org/W70975097","https://openalex.org/W1521064364","https://openalex.org/W1557952530","https://openalex.org/W1569614731","https://openalex.org/W1579279110","https://openalex.org/W1607307044","https://openalex.org/W1895191496","https://openalex.org/W1922126009","https://openalex.org/W1978729128","https://openalex.org/W1981283549","https://openalex.org/W1998042868","https://openalex.org/W2004920872","https://openalex.org/W2046932483","https://openalex.org/W2048716406","https://openalex.org/W2049951199","https://openalex.org/W2053317383","https://openalex.org/W2069472161","https://openalex.org/W2076014259","https://openalex.org/W2082890803","https://openalex.org/W2095705004","https://openalex.org/W2122221966","https://openalex.org/W2131673214","https://openalex.org/W2133319764","https://openalex.org/W2144499799","https://openalex.org/W2144506857","https://openalex.org/W2153182373","https://openalex.org/W2404161323","https://openalex.org/W2962790387","https://openalex.org/W2963718330","https://openalex.org/W2963911037","https://openalex.org/W3143797459","https://openalex.org/W4213116910","https://openalex.org/W6602936574","https://openalex.org/W6631165897","https://openalex.org/W6633516429","https://openalex.org/W6634063554","https://openalex.org/W6636382570","https://openalex.org/W6638444622","https://openalex.org/W6674330103","https://openalex.org/W6679852000","https://openalex.org/W6682488149"],"related_works":["https://openalex.org/W2140536630","https://openalex.org/W3195005284","https://openalex.org/W2391730868","https://openalex.org/W4394659737","https://openalex.org/W2759814045","https://openalex.org/W2118055728","https://openalex.org/W4399756845","https://openalex.org/W2736760277","https://openalex.org/W4386940087","https://openalex.org/W4386931226"],"abstract_inverted_index":{"Natural":[0],"scene":[1,94],"text":[2,91],"recognition":[3],"has":[4],"proved":[5],"to":[6,10,18,113],"be":[7],"challenging":[8],"due":[9],"the":[11,34,53,77,82,90,93,97,114],"unconstrained":[12],"wild":[13],"conditions.":[14],"In":[15,76],"this":[16,20],"paper,":[17],"solve":[19],"problem":[21],"we":[22,51,79],"propose":[23],"a":[24,69],"method":[25],"which":[26],"first":[27],"detects":[28],"and":[29,55,87,96,106],"recognizes":[30],"characters":[31],"by":[32,45],"utilizing":[33],"high":[35,72],"performance":[36,112],"Convolutional":[37],"Neural":[38],"Network":[39],"(CNN).":[40],"Then":[41],"for":[42,66,99],"post-processing,":[43],"inspired":[44],"its":[46],"success":[47],"in":[48,92],"speech":[49],"recognition,":[50],"employ":[52],"efficient":[54],"flexible":[56],"Weight":[57],"Finite":[58],"State":[59],"Transducer":[60],"(WFST)":[61],"based":[62],"word":[63],"labeling":[64],"model":[65],"incorporation":[67],"with":[68],"lexicon":[70],"or":[71,110],"order":[73],"language":[74],"model.":[75],"experiments":[78],"show":[80,108],"that":[81],"proposed":[83],"approach":[84],"can":[85],"correctly":[86],"robustly":[88],"recognize":[89],"images":[95],"results":[98],"serveral":[100],"public":[101],"datasets":[102],"(ICDAR":[103],"2003,":[104],"SVT":[105],"IIIT5K)":[107],"comparable":[109],"superior":[111],"state-of-the-art":[115],"algorithms.":[116]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1}],"updated_date":"2026-03-14T06:41:57.775601","created_date":"2025-10-10T00:00:00"}
