{"id":"https://openalex.org/W3089298195","doi":"https://doi.org/10.1109/access.2020.3025221","title":"A Text Detection Algorithm for Image of Student Exercises Based on CTPN and Enhanced YOLOv3","display_name":"A Text Detection Algorithm for Image of Student Exercises Based on CTPN and Enhanced YOLOv3","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W3089298195","doi":"https://doi.org/10.1109/access.2020.3025221","mag":"3089298195"},"language":"en","primary_location":{"id":"doi:10.1109/access.2020.3025221","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.3025221","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09200481.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09200481.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101936719","display_name":"Langcai Cao","orcid":"https://orcid.org/0000-0001-6388-1413"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Langcai Cao","raw_affiliation_strings":["Xiamen Key Laboratory of Big Data Intelligent Analysis and Decision, Xiamen, China","Xiamen University, Xiamen, China"],"raw_orcid":"https://orcid.org/0000-0001-6388-1413","affiliations":[{"raw_affiliation_string":"Xiamen Key Laboratory of Big Data Intelligent Analysis and Decision, Xiamen, China","institution_ids":[]},{"raw_affiliation_string":"Xiamen University, Xiamen, China","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101457260","display_name":"Hongwei Li","orcid":"https://orcid.org/0000-0002-8315-8297"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongwei Li","raw_affiliation_strings":["Xiamen University, Xiamen, China"],"raw_orcid":"https://orcid.org/0000-0002-8315-8297","affiliations":[{"raw_affiliation_string":"Xiamen University, Xiamen, China","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032781776","display_name":"Rongbiao Xie","orcid":"https://orcid.org/0000-0002-2129-9094"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Rongbiao Xie","raw_affiliation_strings":["Xiamen University, Xiamen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xiamen University, Xiamen, China","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5087189152","display_name":"Jinrong Zhu","orcid":"https://orcid.org/0000-0001-6154-2899"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinrong Zhu","raw_affiliation_strings":["Xiamen University, Xiamen, China"],"raw_orcid":"https://orcid.org/0000-0001-6154-2899","affiliations":[{"raw_affiliation_string":"Xiamen University, Xiamen, China","institution_ids":["https://openalex.org/I191208505"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.9789,"has_fulltext":true,"cited_by_count":18,"citation_normalized_percentile":{"value":0.78561973,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":98},"biblio":{"volume":"8","issue":null,"first_page":"176924","last_page":"176934"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10601","display_name":"Handwritten Text Recognition Techniques","score":0.9991000294685364,"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":0.9991000294685364,"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":0.9927999973297119,"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"}},{"id":"https://openalex.org/T11398","display_name":"Hand Gesture Recognition Systems","score":0.9889000058174133,"subfield":{"id":"https://openalex.org/subfields/1709","display_name":"Human-Computer Interaction"},"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":0.7834315299987793},{"id":"https://openalex.org/keywords/image-stitching","display_name":"Image stitching","score":0.7267023921012878},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.6831059455871582},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6202202439308167},{"id":"https://openalex.org/keywords/text-detection","display_name":"Text detection","score":0.5789929628372192},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.5697107315063477},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4776950180530548},{"id":"https://openalex.org/keywords/relation","display_name":"Relation (database)","score":0.4682924151420593},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.44910192489624023},{"id":"https://openalex.org/keywords/character","display_name":"Character (mathematics)","score":0.43936699628829956},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.43789955973625183},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.421177476644516},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.3565821051597595},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.34858861565589905},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.2654155492782593},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.19040027260780334}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7834315299987793},{"id":"https://openalex.org/C29081049","wikidata":"https://www.wikidata.org/wiki/Q1364242","display_name":"Image stitching","level":2,"score":0.7267023921012878},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.6831059455871582},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6202202439308167},{"id":"https://openalex.org/C2983589003","wikidata":"https://www.wikidata.org/wiki/Q167555","display_name":"Text detection","level":3,"score":0.5789929628372192},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.5697107315063477},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4776950180530548},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.4682924151420593},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.44910192489624023},{"id":"https://openalex.org/C2780861071","wikidata":"https://www.wikidata.org/wiki/Q1062934","display_name":"Character (mathematics)","level":2,"score":0.43936699628829956},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.43789955973625183},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.421177476644516},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3565821051597595},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.34858861565589905},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2654155492782593},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.19040027260780334},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"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/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2020.3025221","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.3025221","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09200481.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:57dd272ec8fa4dcc9d1609202369f9df","is_oa":true,"landing_page_url":"https://doaj.org/article/57dd272ec8fa4dcc9d1609202369f9df","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 8, Pp 176924-176934 (2020)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2020.3025221","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.3025221","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09200481.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Quality Education","score":0.8500000238418579,"id":"https://metadata.un.org/sdg/4"}],"awards":[{"id":"https://openalex.org/G7833536237","display_name":"\u57fa\u4e8e\u503c\u7b49\u4ef7\u7684\u4ea4\u4e92\u5f0f\u52a8\u6001\u5f71\u54cd\u56fe\u7684\u6c42\u89e3\u65b9\u6cd5\u7814\u7a76\u4e0e\u5e94\u7528","funder_award_id":"61772442","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3089298195.pdf","grobid_xml":"https://content.openalex.org/works/W3089298195.grobid-xml"},"referenced_works_count":34,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1536680647","https://openalex.org/W1903029394","https://openalex.org/W2001642682","https://openalex.org/W2036435040","https://openalex.org/W2061802763","https://openalex.org/W2102605133","https://openalex.org/W2114449851","https://openalex.org/W2124404372","https://openalex.org/W2142159465","https://openalex.org/W2145592737","https://openalex.org/W2163605009","https://openalex.org/W2193145675","https://openalex.org/W2339589954","https://openalex.org/W2519818067","https://openalex.org/W2565639579","https://openalex.org/W2570343428","https://openalex.org/W2612624696","https://openalex.org/W2765820394","https://openalex.org/W2784050770","https://openalex.org/W2796347433","https://openalex.org/W2909425146","https://openalex.org/W2919538836","https://openalex.org/W2963037989","https://openalex.org/W2963150697","https://openalex.org/W2963647456","https://openalex.org/W2967615747","https://openalex.org/W2994503831","https://openalex.org/W2999952507","https://openalex.org/W3102695566","https://openalex.org/W3106250896","https://openalex.org/W4293584584","https://openalex.org/W6684191040","https://openalex.org/W6750227808"],"related_works":["https://openalex.org/W68020613","https://openalex.org/W1973382465","https://openalex.org/W2091466534","https://openalex.org/W4255291540","https://openalex.org/W2949096641","https://openalex.org/W2970686063","https://openalex.org/W4320729701","https://openalex.org/W3210378990","https://openalex.org/W3034745255","https://openalex.org/W4254103348"],"abstract_inverted_index":{"Intelligent":[0],"learning":[1,8],"system":[2],"(ILS)":[3],"has":[4,188],"become":[5],"a":[6,52,90,111,115,120,185],"popular":[7],"tool":[9],"for":[10,34,93,192],"students.":[11,35],"It":[12],"can":[13,30,64,155],"collect":[14],"students'":[15,42,197],"wrong":[16],"questions":[17],"in":[18,48,70,169,196],"exercises":[19,33,43],"and":[20,46,74,82,118,147,175,179],"dig":[21],"out":[22],"their":[23,75],"unskilled":[24],"knowledge":[25],"points":[26],"so":[27],"that":[28,59,152],"it":[29],"recommend":[31],"personalized":[32],"Detecting":[36],"text":[37,56,61,68,94,117,125,144,173,194],"accurately":[38],"from":[39,110],"images":[40],"of":[41,55,171],"is":[44,58],"significant":[45],"essential":[47],"an":[49,71],"ILS.":[50],"However,":[51],"big":[53],"challenge":[54],"detection":[57,62,76,145,157],"traditional":[60],"algorithms":[63],"not":[65],"detect":[66],"complete":[67],"lines":[69,126,195],"exercise":[72,149,172],"scene,":[73],"box":[77],"always":[78],"splits":[79],"between":[80],"Chinese":[81,143],"mathematical":[83],"symbols.":[84],"In":[85,159],"this":[86],"article,":[87],"we":[88,161],"propose":[89],"deep-learning-based":[91],"approach":[92],"detection,":[95,174],"which":[96,132,187,203],"improves":[97,133],"You":[98],"Only":[99],"Look":[100],"Once":[101],"version":[102],"3":[103],"(YOLOv3)":[104],"by":[105,136],"changing":[106],"the":[107,129,134,190],"regression":[108],"object":[109],"single":[112],"character":[113],"to":[114,123],"fixed-width":[116],"applies":[119],"stitching":[121],"strategy":[122],"construct":[124],"based":[127],"on":[128,140],"relation":[130],"matrix,":[131],"accuracy":[135],"9.8%.":[137],"Experimental":[138],"results":[139],"both":[141],"RCTW":[142],"dataset":[146],"real":[148],"scenario":[150],"show":[151],"our":[153,163],"model":[154],"improve":[156],"effectiveness.":[158],"addition,":[160],"compare":[162],"method":[164],"with":[165],"two":[166],"state-of-the-art":[167],"approaches":[168],"applications":[170],"discuss":[176],"its":[177],"capability":[178],"limitations.":[180],"We":[181],"have":[182],"also":[183],"provided":[184],"platform":[186],"implemented":[189],"proposal":[191],"detecting":[193],"daily":[198],"homework":[199],"or":[200],"examination":[201],"papers,":[202],"enhances":[204],"user":[205],"experience":[206],"well.":[207]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":3}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}