{"id":"https://openalex.org/W2977807402","doi":"https://doi.org/10.1109/ijcnn.2019.8851959","title":"Text Attention and Focal Negative Loss for Scene Text Detection","display_name":"Text Attention and Focal Negative Loss for Scene Text Detection","publication_year":2019,"publication_date":"2019-07-01","ids":{"openalex":"https://openalex.org/W2977807402","doi":"https://doi.org/10.1109/ijcnn.2019.8851959","mag":"2977807402"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2019.8851959","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2019.8851959","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Joint Conference on Neural Networks (IJCNN)","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/A5023759358","display_name":"Randong Huang","orcid":null},"institutions":[{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Randong Huang","raw_affiliation_strings":["University of Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"University of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210165038"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100439920","display_name":"Bo Xu","orcid":"https://orcid.org/0000-0001-6049-8005"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210112150","display_name":"Institute of Automation","ror":"https://ror.org/022c3hy66","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210112150"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bo Xu","raw_affiliation_strings":["Chinese Academy of Sciences, Institute of Automation, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Chinese Academy of Sciences, Institute of Automation, Beijing, China","institution_ids":["https://openalex.org/I4210112150","https://openalex.org/I19820366"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5023759358"],"corresponding_institution_ids":["https://openalex.org/I4210165038"],"apc_list":null,"apc_paid":null,"fwci":0.2024,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.54278412,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"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":0.9876999855041504,"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":0.98580002784729,"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":0.7763866186141968},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6132166385650635},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5800428986549377},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.5735850930213928},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5459722876548767},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5145115852355957},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4748380780220032},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.4171736240386963},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3376745581626892}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7763866186141968},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6132166385650635},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5800428986549377},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.5735850930213928},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5459722876548767},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5145115852355957},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4748380780220032},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.4171736240386963},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3376745581626892},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C78458016","wikidata":"https://www.wikidata.org/wiki/Q840400","display_name":"Evolutionary biology","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn.2019.8851959","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2019.8851959","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Joint Conference on Neural Networks (IJCNN)","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":64,"referenced_works":["https://openalex.org/W117491841","https://openalex.org/W639708223","https://openalex.org/W1536680647","https://openalex.org/W1922126009","https://openalex.org/W1972065312","https://openalex.org/W1988461287","https://openalex.org/W2006653496","https://openalex.org/W2008806374","https://openalex.org/W2019478948","https://openalex.org/W2061802763","https://openalex.org/W2102605133","https://openalex.org/W2129987527","https://openalex.org/W2144554289","https://openalex.org/W2179352600","https://openalex.org/W2194775991","https://openalex.org/W2216125271","https://openalex.org/W2339589954","https://openalex.org/W2343052201","https://openalex.org/W2395360388","https://openalex.org/W2464918637","https://openalex.org/W2468724597","https://openalex.org/W2472159136","https://openalex.org/W2504335775","https://openalex.org/W2519818067","https://openalex.org/W2550687635","https://openalex.org/W2555182955","https://openalex.org/W2593539516","https://openalex.org/W2604243686","https://openalex.org/W2604735854","https://openalex.org/W2605076167","https://openalex.org/W2605982830","https://openalex.org/W2613718673","https://openalex.org/W2831607544","https://openalex.org/W2895077992","https://openalex.org/W2953384591","https://openalex.org/W2962773189","https://openalex.org/W2962790387","https://openalex.org/W2962810613","https://openalex.org/W2963037989","https://openalex.org/W2963150697","https://openalex.org/W2963187132","https://openalex.org/W2963195262","https://openalex.org/W2963351448","https://openalex.org/W2963516811","https://openalex.org/W2963647456","https://openalex.org/W2963840241","https://openalex.org/W2963977642","https://openalex.org/W2964018263","https://openalex.org/W3098090606","https://openalex.org/W3106228955","https://openalex.org/W3106250896","https://openalex.org/W6620707391","https://openalex.org/W6631782140","https://openalex.org/W6640226783","https://openalex.org/W6676338569","https://openalex.org/W6679461745","https://openalex.org/W6704278359","https://openalex.org/W6712036928","https://openalex.org/W6713134421","https://openalex.org/W6719590338","https://openalex.org/W6729791593","https://openalex.org/W6730003152","https://openalex.org/W6752534923","https://openalex.org/W6785652829"],"related_works":["https://openalex.org/W4293226380","https://openalex.org/W4375867731","https://openalex.org/W2055243143","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3103566983","https://openalex.org/W3167935049","https://openalex.org/W3029198973"],"abstract_inverted_index":{"This":[0],"paper":[1],"proposes":[2],"a":[3,8,45,94],"novel":[4],"attention":[5,18,29,34,137,161],"mechanism":[6,19],"and":[7,59,139,147,163,178],"fancy":[9],"loss":[10,96,105,112],"function":[11,106],"for":[12],"scene":[13,149],"text":[14,24,63,72,136,150,160],"detectors.":[15],"Specifically,":[16],"the":[17,23,40,56,62,84,111,119,132,145,167],"can":[20,122,165],"effectively":[21],"identify":[22],"regions":[25],"by":[26,171],"learning":[27],"an":[28],"mask":[30,35],"automatically.":[31],"The":[32,103,153],"fine-grained":[33],"is":[36,107],"directly":[37],"incorporated":[38],"into":[39,144],"convolutional":[41],"feature":[42,51,68],"maps":[43,69],"of":[44,77,134,169,173],"neural":[46],"network":[47],"to":[48,82,109,114],"produce":[49],"graininess-aware":[50,67],"maps,":[52],"which":[53],"essentially":[54],"obstruct":[55],"background":[57],"inference":[58],"especially":[60],"emphasize":[61],"regions.":[64],"Therefore,":[65],"our":[66,135,159],"concentrate":[70],"on":[71,126,175,180],"regions,":[73],"in":[74],"especial":[75],"those":[76],"exceedingly":[78],"small":[79],"size.":[80],"Additionally,":[81],"address":[83],"extreme":[85],"text-background":[86],"class":[87],"imbalance":[88],"during":[89],"training,":[90],"we":[91,141],"also":[92],"propose":[93],"newfangled":[95],"function,":[97],"named":[98],"Focal":[99],"Negative":[100],"Loss":[101],"(FNL).":[102],"proposed":[104,120],"able":[108],"down-weight":[110],"assigned":[113],"easy":[115],"negative":[116,128],"samples.":[117,129],"Consequently,":[118],"FNL":[121,164],"make":[123],"training":[124],"focused":[125],"hard":[127],"To":[130],"evaluate":[131],"effectiveness":[133],"module":[138,162],"FNL,":[140],"integrate":[142],"them":[143],"efficient":[146],"accurate":[148],"detector":[151],"(EAST).":[152],"comprehensive":[154],"experimental":[155],"results":[156],"demonstrate":[157],"that":[158],"increase":[166],"performance":[168],"EAST":[170],"F-score":[172],"3.98%":[174],"ICDAR2015":[176],"dataset":[177],"1.87%":[179],"MSRA-TD500":[181],"dataset.":[182]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
