{"id":"https://openalex.org/W2036887016","doi":"https://doi.org/10.1109/icip.2012.6467245","title":"Context-based text detection in natural scenes","display_name":"Context-based text detection in natural scenes","publication_year":2012,"publication_date":"2012-09-01","ids":{"openalex":"https://openalex.org/W2036887016","doi":"https://doi.org/10.1109/icip.2012.6467245","mag":"2036887016"},"language":"en","primary_location":{"id":"doi:10.1109/icip.2012.6467245","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2012.6467245","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2012 19th IEEE International Conference on Image Processing","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/A5010944460","display_name":"Yuning Du","orcid":"https://orcid.org/0009-0007-4995-5472"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuning Du","raw_affiliation_strings":["Computer Science & Technology Department, Tsinghua University","Computer Science and Technology Dept., Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science & Technology Department, Tsinghua University","institution_ids":["https://openalex.org/I99065089"]},{"raw_affiliation_string":"Computer Science and Technology Dept., Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069092263","display_name":"Genquan Duan","orcid":null},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Genquan Duan","raw_affiliation_strings":["Computer Science & Technology Department, Tsinghua University","Computer Science and Technology Dept., Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science & Technology Department, Tsinghua University","institution_ids":["https://openalex.org/I99065089"]},{"raw_affiliation_string":"Computer Science and Technology Dept., Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5074577884","display_name":"Haizhou Ai","orcid":"https://orcid.org/0000-0002-0166-5755"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haizhou Ai","raw_affiliation_strings":["Computer Science & Technology Department, Tsinghua University","Computer Science and Technology Dept., Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science & Technology Department, Tsinghua University","institution_ids":["https://openalex.org/I99065089"]},{"raw_affiliation_string":"Computer Science and Technology Dept., Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":1.3535,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":{"value":0.83856268,"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":"1857","last_page":"1860"},"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/T12707","display_name":"Vehicle License Plate Recognition","score":0.9958999752998352,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9864000082015991,"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":0.7712164521217346},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6591182947158813},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.559729814529419},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5457640290260315},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5381473302841187},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.517820417881012},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5110217332839966},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5048220753669739},{"id":"https://openalex.org/keywords/text-detection","display_name":"Text detection","score":0.46585163474082947},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.45437854528427124},{"id":"https://openalex.org/keywords/speedup","display_name":"Speedup","score":0.4354042112827301},{"id":"https://openalex.org/keywords/crfs","display_name":"CRFS","score":0.41849035024642944},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3181275725364685},{"id":"https://openalex.org/keywords/cartography","display_name":"Cartography","score":0.09038084745407104},{"id":"https://openalex.org/keywords/conditional-random-field","display_name":"Conditional random field","score":0.07561665773391724}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7712164521217346},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6591182947158813},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.559729814529419},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5457640290260315},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5381473302841187},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.517820417881012},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5110217332839966},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5048220753669739},{"id":"https://openalex.org/C2983589003","wikidata":"https://www.wikidata.org/wiki/Q167555","display_name":"Text detection","level":3,"score":0.46585163474082947},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.45437854528427124},{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.4354042112827301},{"id":"https://openalex.org/C2775953691","wikidata":"https://www.wikidata.org/wiki/Q5013874","display_name":"CRFS","level":3,"score":0.41849035024642944},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3181275725364685},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.09038084745407104},{"id":"https://openalex.org/C152565575","wikidata":"https://www.wikidata.org/wiki/Q1124538","display_name":"Conditional random field","level":2,"score":0.07561665773391724},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","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/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/icip.2012.6467245","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2012.6467245","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2012 19th IEEE International Conference on Image Processing","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.294.6109","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.294.6109","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://media.cs.tsinghua.edu.cn/~imagevision/papers/[2012]0001857-icip2012-du.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W1600276254","https://openalex.org/W2018451638","https://openalex.org/W2022897019","https://openalex.org/W2065029203","https://openalex.org/W2131163834","https://openalex.org/W2137718414","https://openalex.org/W2140132917","https://openalex.org/W2142159465","https://openalex.org/W2153635508","https://openalex.org/W2165569569","https://openalex.org/W2166949156","https://openalex.org/W2169696215","https://openalex.org/W3120421331","https://openalex.org/W6654713941","https://openalex.org/W6655798865","https://openalex.org/W6679413825"],"related_works":["https://openalex.org/W4295602020","https://openalex.org/W3022161193","https://openalex.org/W2800507189","https://openalex.org/W2058965144","https://openalex.org/W2164382479","https://openalex.org/W2361832341","https://openalex.org/W165283731","https://openalex.org/W2132346352","https://openalex.org/W2104929832","https://openalex.org/W2772211479"],"abstract_inverted_index":{"Text":[0],"detection":[1],"in":[2,72,86],"natural":[3],"scenes":[4],"is":[5,28,39],"fundamental":[6],"for":[7,19],"text":[8,23,54,96],"image":[9],"analysis.":[10],"In":[11],"this":[12],"paper,":[13],"we":[14,30,60],"propose":[15],"a":[16,32,65],"context-based":[17,66],"approach":[18,108,128],"robust":[20],"and":[21,49,125],"fast":[22],"detection.":[24],"Our":[25],"main":[26],"contribution":[27],"that":[29,106],"introduce":[31],"new":[33],"concept":[34],"of":[35,53,136],"key":[36,70],"region,":[37],"which":[38,89],"described":[40],"with":[41,114,133],"context":[42,58],"according":[43],"to":[44,63,68],"stroke":[45,120],"properties,":[46],"appearance":[47],"consistency":[48],"specific":[50],"spatial":[51],"distribution":[52],"line.":[55],"With":[56],"such":[57],"descriptors,":[59],"adopt":[61],"SVM":[62],"learn":[64],"classifier":[67],"find":[69],"regions":[71,77],"candidate":[73,76],"regions.":[74],"Therein,":[75],"are":[78,90],"connected":[79],"components":[80],"generated":[81],"by":[82,92],"local":[83],"binarization":[84],"algorithm":[85],"the":[87,115,119,126],"areas,":[88],"detected":[91],"an":[93],"offline":[94],"learned":[95],"patch":[97],"detector.":[98],"Experimental":[99],"results":[100],"on":[101,130],"two":[102],"benchmark":[103],"datasets":[104],"demonstrate":[105],"our":[107],"has":[109],"achieved":[110],"competitive":[111],"performances":[112],"compared":[113],"state-of-the-art":[116],"algorithms":[117],"including":[118],"width":[121],"transform":[122],"(SWT)":[123],"[1]":[124],"hybrid":[127],"based":[129],"CRFs":[131],"[2]":[132],"speedup":[134],"rates":[135],"about":[137],"1.7x~4.4x.":[138]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":2},{"year":2015,"cited_by_count":1},{"year":2014,"cited_by_count":4},{"year":2013,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
