{"id":"https://openalex.org/W3160610138","doi":"https://doi.org/10.1109/icpr48806.2021.9412325","title":"An Accurate Threshold Insensitive Kernel Detector for Arbitrary Shaped Text","display_name":"An Accurate Threshold Insensitive Kernel Detector for Arbitrary Shaped Text","publication_year":2021,"publication_date":"2021-01-10","ids":{"openalex":"https://openalex.org/W3160610138","doi":"https://doi.org/10.1109/icpr48806.2021.9412325","mag":"3160610138"},"language":"en","primary_location":{"id":"doi:10.1109/icpr48806.2021.9412325","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr48806.2021.9412325","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/A5046311056","display_name":"Xijun Qian","orcid":null},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xijun Qian","raw_affiliation_strings":["State Key Laboratory for Novel Software Technology of Nanjing University, Nanjing University, Nanjing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Key Laboratory for Novel Software Technology of Nanjing University, Nanjing University, Nanjing, China","institution_ids":["https://openalex.org/I881766915"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100376138","display_name":"Yifan Liu","orcid":"https://orcid.org/0000-0001-8866-6322"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yifan Liu","raw_affiliation_strings":["State Key Laboratory for Novel Software Technology of Nanjing University, Nanjing University, Nanjing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Key Laboratory for Novel Software Technology of Nanjing University, Nanjing University, Nanjing, China","institution_ids":["https://openalex.org/I881766915"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5111673780","display_name":"Yu-Bin Yang","orcid":"https://orcid.org/0000-0002-3764-1114"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yubin Yang","raw_affiliation_strings":["State Key Laboratory for Novel Software Technology of Nanjing University, Nanjing University, Nanjing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Key Laboratory for Novel Software Technology of Nanjing University, Nanjing University, Nanjing, China","institution_ids":["https://openalex.org/I881766915"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I881766915"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.04474609,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"27","issue":null,"first_page":"3011","last_page":"3018"},"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/T14339","display_name":"Image Processing and 3D Reconstruction","score":0.9926999807357788,"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":0.9857000112533569,"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/kernel","display_name":"Kernel (algebra)","score":0.7070591449737549},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6380361318588257},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.5773569345474243},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.565149188041687},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5571865439414978},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5545010566711426},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.5541368722915649},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.5276433825492859},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5043710470199585},{"id":"https://openalex.org/keywords/stability","display_name":"Stability (learning theory)","score":0.4932207465171814},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4555671215057373},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4549597501754761},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.439997136592865},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.41840261220932007},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.376361221075058},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.29841190576553345},{"id":"https://openalex.org/keywords/discrete-mathematics","display_name":"Discrete mathematics","score":0.1724403202533722},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.13971593976020813},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.12667939066886902}],"concepts":[{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.7070591449737549},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6380361318588257},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.5773569345474243},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.565149188041687},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5571865439414978},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5545010566711426},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.5541368722915649},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.5276433825492859},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5043710470199585},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.4932207465171814},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4555671215057373},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4549597501754761},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.439997136592865},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.41840261220932007},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.376361221075058},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.29841190576553345},{"id":"https://openalex.org/C118615104","wikidata":"https://www.wikidata.org/wiki/Q121416","display_name":"Discrete mathematics","level":1,"score":0.1724403202533722},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.13971593976020813},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.12667939066886902},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"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/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icpr48806.2021.9412325","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr48806.2021.9412325","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":0.6499999761581421,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"}],"awards":[{"id":"https://openalex.org/G6356681570","display_name":null,"funder_award_id":"61673204","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7619885634","display_name":null,"funder_award_id":"14380046","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":46,"referenced_works":["https://openalex.org/W1972065312","https://openalex.org/W1988461287","https://openalex.org/W2074849287","https://openalex.org/W2144554289","https://openalex.org/W2302255633","https://openalex.org/W2560023338","https://openalex.org/W2565639579","https://openalex.org/W2593539516","https://openalex.org/W2605076167","https://openalex.org/W2605982830","https://openalex.org/W2725486421","https://openalex.org/W2772800855","https://openalex.org/W2784050770","https://openalex.org/W2810028092","https://openalex.org/W2831607544","https://openalex.org/W2899771611","https://openalex.org/W2901776468","https://openalex.org/W2902494497","https://openalex.org/W2909425146","https://openalex.org/W2953106684","https://openalex.org/W2953894958","https://openalex.org/W2962810613","https://openalex.org/W2962914239","https://openalex.org/W2963150697","https://openalex.org/W2963161243","https://openalex.org/W2963299604","https://openalex.org/W2963353821","https://openalex.org/W2963398399","https://openalex.org/W2963647456","https://openalex.org/W2963840241","https://openalex.org/W2964309882","https://openalex.org/W2966926453","https://openalex.org/W2967615747","https://openalex.org/W2998621280","https://openalex.org/W3003868038","https://openalex.org/W3102695566","https://openalex.org/W3106228955","https://openalex.org/W6620707391","https://openalex.org/W6642972425","https://openalex.org/W6698183232","https://openalex.org/W6739844568","https://openalex.org/W6746206475","https://openalex.org/W6748481559","https://openalex.org/W6752143097","https://openalex.org/W6756361325","https://openalex.org/W6770629681"],"related_works":["https://openalex.org/W3006513224","https://openalex.org/W2046456988","https://openalex.org/W2357409937","https://openalex.org/W2978674666","https://openalex.org/W2074430941","https://openalex.org/W2085033728","https://openalex.org/W4285411112","https://openalex.org/W2113096305","https://openalex.org/W2171299904","https://openalex.org/W2067272521"],"abstract_inverted_index":{"Recently,":[0],"segmentation-based":[1],"methods":[2],"are":[3],"popular":[4],"in":[5,82,102,143],"scene":[6,18],"text":[7,19,27,39,57,95],"detection":[8],"due":[9],"to":[10,91,150],"the":[11,36,89,113,123,141,163],"segmentation":[12],"results":[13],"that":[14,35,110],"can":[15,87,154],"easily":[16],"represent":[17],"of":[20,38,117,130],"arbitrary":[21,55],"shapes.":[22],"However,":[23],"previous":[24],"works":[25],"view":[26],"instances":[28],"as":[29],"normal":[30,42],"objects.":[31,43],"It":[32,106],"is":[33,107],"obvious":[34],"edge":[37],"differs":[40],"from":[41,148],"In":[44],"this":[45],"paper,":[46],"we":[47,111,139],"propose":[48],"a":[49,62,69,83,127,157],"threshold":[50,142],"insensitive":[51],"kernel":[52],"detector":[53],"for":[54],"shaped":[56],"called":[58,73],"TIKD,":[59],"which":[60],"includes":[61],"simple":[63],"but":[64],"stable":[65,158],"base":[66],"model":[67],"and":[68,104],"new":[70],"learning":[71],"weight":[72],"Decay":[74],"Loss":[75],"Weight":[76],"(DLW).":[77],"By":[78],"suppressing":[79],"outlier":[80],"pixels":[81],"gradual":[84],"way,":[85],"DLW":[86],"lead":[88],"network":[90],"learn":[92],"more":[93],"accurate":[94],"instances.":[96],"Our":[97],"method":[98,153],"shows":[99],"great":[100],"power":[101],"accuracy":[103],"stability.":[105],"worth":[108],"mentioning":[109],"achieve":[112,156],"precision,":[114],"recall,":[115],"f-measure":[116,159],"88.7%,":[118],"83.7%,":[119],"86.1%":[120],"respectively":[121],"on":[122,162],"Total-Text":[124,164],"dataset,":[125],"with":[126],"fast":[128],"speed":[129],"16.3":[131],"frames":[132],"per":[133],"second.":[134],"What's":[135],"more,":[136],"even":[137],"if":[138],"set":[140],"an":[144],"extreme":[145],"situation":[146],"range":[147],"0.1":[149],"0.9,":[151],"our":[152],"always":[155],"over":[160],"79.9%":[161],"dataset.":[165]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
