{"id":"https://openalex.org/W3172613296","doi":"https://doi.org/10.1145/3460620.3460623","title":"Detection of Text from Video with Customized Trained Anatomy","display_name":"Detection of Text from Video with Customized Trained Anatomy","publication_year":2021,"publication_date":"2021-04-05","ids":{"openalex":"https://openalex.org/W3172613296","doi":"https://doi.org/10.1145/3460620.3460623","mag":"3172613296"},"language":"en","primary_location":{"id":"doi:10.1145/3460620.3460623","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3460620.3460623","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Conference on Data Science, E-learning and Information Systems 2021","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/A5006748993","display_name":"Manasa Devi Devi Mortha","orcid":null},"institutions":[{"id":"https://openalex.org/I10874241","display_name":"Jawaharlal Nehru Technological University, Hyderabad","ror":"https://ror.org/002tchr49","country_code":"IN","type":"education","lineage":["https://openalex.org/I10874241"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Manasa Devi Mortha","raw_affiliation_strings":["JNTUH, India"],"affiliations":[{"raw_affiliation_string":"JNTUH, India","institution_ids":["https://openalex.org/I10874241"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004277091","display_name":"Seetha Maddala","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Seetha Maddala","raw_affiliation_strings":["GNITS, India"],"affiliations":[{"raw_affiliation_string":"GNITS, India","institution_ids":[]}]},{"author_position":"last","author":{"id":null,"display_name":"Vishwanadha Raju","orcid":null},"institutions":[{"id":"https://openalex.org/I10874241","display_name":"Jawaharlal Nehru Technological University, Hyderabad","ror":"https://ror.org/002tchr49","country_code":"IN","type":"education","lineage":["https://openalex.org/I10874241"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Vishwanadha Raju","raw_affiliation_strings":["JNTUH, India"],"affiliations":[{"raw_affiliation_string":"JNTUH, India","institution_ids":["https://openalex.org/I10874241"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5006748993"],"corresponding_institution_ids":["https://openalex.org/I10874241"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.06474332,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"12","last_page":"17"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10601","display_name":"Handwritten Text Recognition Techniques","score":0.9995999932289124,"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":0.9995999932289124,"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":0.9936000108718872,"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/T11439","display_name":"Video Analysis and Summarization","score":0.9858999848365784,"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":0.847545862197876},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7088577747344971},{"id":"https://openalex.org/keywords/minimum-bounding-box","display_name":"Minimum bounding box","score":0.5940039753913879},{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.5588060021400452},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5266566276550293},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4993281364440918},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.4892742335796356},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.48762908577919006},{"id":"https://openalex.org/keywords/contrast","display_name":"Contrast (vision)","score":0.4594572186470032},{"id":"https://openalex.org/keywords/connectionism","display_name":"Connectionism","score":0.4582386016845703},{"id":"https://openalex.org/keywords/architecture","display_name":"Architecture","score":0.4506455063819885},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.25856325030326843},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.21101391315460205}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.847545862197876},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7088577747344971},{"id":"https://openalex.org/C147037132","wikidata":"https://www.wikidata.org/wiki/Q6865426","display_name":"Minimum bounding box","level":3,"score":0.5940039753913879},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.5588060021400452},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5266566276550293},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4993281364440918},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.4892742335796356},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.48762908577919006},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.4594572186470032},{"id":"https://openalex.org/C8521452","wikidata":"https://www.wikidata.org/wiki/Q203790","display_name":"Connectionism","level":3,"score":0.4582386016845703},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.4506455063819885},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.25856325030326843},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.21101391315460205},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"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/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0},{"id":"https://openalex.org/C153349607","wikidata":"https://www.wikidata.org/wiki/Q36649","display_name":"Visual arts","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3460620.3460623","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3460620.3460623","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Conference on Data Science, E-learning and Information Systems 2021","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4399999976158142,"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":79,"referenced_works":["https://openalex.org/W195569725","https://openalex.org/W1888914491","https://openalex.org/W1989902600","https://openalex.org/W1998830943","https://openalex.org/W2008678252","https://openalex.org/W2023426496","https://openalex.org/W2047018717","https://openalex.org/W2090842716","https://openalex.org/W2095164481","https://openalex.org/W2116377106","https://openalex.org/W2126664049","https://openalex.org/W2189916328","https://openalex.org/W2190538926","https://openalex.org/W2191976002","https://openalex.org/W2192642394","https://openalex.org/W2193493184","https://openalex.org/W2195747123","https://openalex.org/W2196370326","https://openalex.org/W2206915033","https://openalex.org/W2220567469","https://openalex.org/W2296380959","https://openalex.org/W2296707350","https://openalex.org/W2306850986","https://openalex.org/W2477350604","https://openalex.org/W2509205439","https://openalex.org/W2510170228","https://openalex.org/W2532549791","https://openalex.org/W2542502579","https://openalex.org/W2549066293","https://openalex.org/W2557266189","https://openalex.org/W2557314464","https://openalex.org/W2557761283","https://openalex.org/W2557779747","https://openalex.org/W2557843173","https://openalex.org/W2558089109","https://openalex.org/W2558623596","https://openalex.org/W2559536865","https://openalex.org/W2559677667","https://openalex.org/W2573910544","https://openalex.org/W2574209248","https://openalex.org/W2596206083","https://openalex.org/W2614188298","https://openalex.org/W2615135581","https://openalex.org/W2759696253","https://openalex.org/W2785629178","https://openalex.org/W2785783567","https://openalex.org/W2786069311","https://openalex.org/W2786998222","https://openalex.org/W2787121309","https://openalex.org/W2787366979","https://openalex.org/W2787701577","https://openalex.org/W2800369870","https://openalex.org/W2809273748","https://openalex.org/W2885080997","https://openalex.org/W2885301212","https://openalex.org/W2885683779","https://openalex.org/W2886560752","https://openalex.org/W2886641087","https://openalex.org/W2886789964","https://openalex.org/W2895343364","https://openalex.org/W2898623405","https://openalex.org/W2898776936","https://openalex.org/W2899074204","https://openalex.org/W2919659556","https://openalex.org/W2920682993","https://openalex.org/W2921483046","https://openalex.org/W2953482279","https://openalex.org/W2953953127","https://openalex.org/W2954439562","https://openalex.org/W2955543247","https://openalex.org/W2964404723","https://openalex.org/W2964742977","https://openalex.org/W2969282196","https://openalex.org/W2997998108","https://openalex.org/W3008735069","https://openalex.org/W3016371023","https://openalex.org/W3101178541","https://openalex.org/W4239107234","https://openalex.org/W4287901611"],"related_works":["https://openalex.org/W2953234277","https://openalex.org/W4205841273","https://openalex.org/W2626256601","https://openalex.org/W4205525690","https://openalex.org/W2900413183","https://openalex.org/W4390975304","https://openalex.org/W1732468982","https://openalex.org/W1761388607","https://openalex.org/W147410782","https://openalex.org/W2745033168"],"abstract_inverted_index":{"With":[0],"the":[1,68,72,95,100,118,124,134,141,147,155],"influence":[2],"of":[3,12,24,39,48,87,102,123,136,157,172],"diverse":[4],"architectures":[5],"like":[6],"ImageNet,":[7],"VGGNet,":[8],"ResNet":[9],"for":[10,22,71],"detection":[11,23,180],"objects":[13,45],"in":[14,26,46,77,169],"images,":[15],"we":[16],"are":[17,107,126],"proposing":[18],"a":[19],"novel":[20],"architecture":[21,70,93],"text":[25,33,119,148],"video.":[27],"It":[28],"is":[29,63],"challenging":[30],"to":[31,36,53,66,98,109,112,116,128,132],"detect":[32],"candidates":[34],"due":[35],"its":[37],"nature":[38],"properties":[40],"that":[41],"varies":[42],"from":[43],"normal":[44],"terms":[47],"contours,":[49],"connectionist,":[50],"size,":[51],"scaling":[52],"motion":[54],"occlusion,":[55],"color":[56],"contrast,":[57],"poor":[58],"illumination,":[59],"etc.":[60],"Also,":[61],"it":[62],"not":[64],"possible":[65],"apply":[67],"existing":[69],"proposed":[73,92,161],"anatomy":[74],"with":[75,140,150],"incompatibility":[76],"targets,":[78],"parameters.":[79],"Hence,":[80],"working":[81],"on":[82,164],"video":[83],"takes":[84],"different":[85,173],"path":[86],"learning":[88,103],"and":[89,153,175,182],"validation.":[90],"The":[91,160],"reads":[94],"temporal":[96],"data":[97,143],"train":[99],"sequence":[101],"features.":[104,187],"These":[105],"features":[106,115,125],"fed":[108,127],"periodic":[110],"connectionist":[111],"learn":[113],"successive":[114],"obtain":[117,133],"candidate.":[120],"Later,":[121],"representation":[122],"regional":[129],"proposal":[130],"network":[131],"regions":[135,149],"interest":[137],"by":[138,145],"comparing":[139],"ground-truth":[142],"followed":[144],"pooling":[146],"bounding":[151],"box":[152],"finding":[154],"probability":[156],"their":[158],"occurrence.":[159],"structure":[162],"evaluated":[163],"an":[165],"ICDAR":[166],"2013":[167],"\u201cText":[168],"Video\u201d":[170],"dataset":[171],"indoor":[174],"outdoor":[176],"videos":[177],"achieves":[178],"high":[179],"rates":[181],"performed":[183],"better":[184],"than":[185],"labeled":[186]},"counts_by_year":[],"updated_date":"2026-04-17T18:11:37.981687","created_date":"2025-10-10T00:00:00"}
