{"id":"https://openalex.org/W2966396984","doi":"https://doi.org/10.1109/iwssip.2019.8787293","title":"Representation Learning and Dissimilarity for Writer Identification","display_name":"Representation Learning and Dissimilarity for Writer Identification","publication_year":2019,"publication_date":"2019-06-01","ids":{"openalex":"https://openalex.org/W2966396984","doi":"https://doi.org/10.1109/iwssip.2019.8787293","mag":"2966396984"},"language":"en","primary_location":{"id":"doi:10.1109/iwssip.2019.8787293","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iwssip.2019.8787293","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Conference on Systems, Signals and Image Processing (IWSSIP)","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/A5070687435","display_name":"Lucas Georges Helal","orcid":null},"institutions":[{"id":"https://openalex.org/I123443094","display_name":"Universidade Estadual de Maring\u00e1","ror":"https://ror.org/04bqqa360","country_code":"BR","type":"education","lineage":["https://openalex.org/I123443094"]}],"countries":["BR"],"is_corresponding":true,"raw_author_name":"Lucas G. Helal","raw_affiliation_strings":["State University of Maringa (UEM), Maringa, Parana, Brazil"],"affiliations":[{"raw_affiliation_string":"State University of Maringa (UEM), Maringa, Parana, Brazil","institution_ids":["https://openalex.org/I123443094"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004057625","display_name":"Diego Bertolini","orcid":"https://orcid.org/0000-0002-6196-4538"},"institutions":[{"id":"https://openalex.org/I1283613182","display_name":"Universidade Tecnol\u00f3gica Federal do Paran\u00e1","ror":"https://ror.org/002v2kq79","country_code":"BR","type":"education","lineage":["https://openalex.org/I1283613182"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Diego Bertolini","raw_affiliation_strings":["Federal Technological University of Paran\u00e1 (UTFPR), Campo Mour\u00e3o, Paran\u00e1, Brazil"],"affiliations":[{"raw_affiliation_string":"Federal Technological University of Paran\u00e1 (UTFPR), Campo Mour\u00e3o, Paran\u00e1, Brazil","institution_ids":["https://openalex.org/I1283613182"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002078182","display_name":"Yandre M. G. Costa","orcid":"https://orcid.org/0000-0002-0630-3171"},"institutions":[{"id":"https://openalex.org/I123443094","display_name":"Universidade Estadual de Maring\u00e1","ror":"https://ror.org/04bqqa360","country_code":"BR","type":"education","lineage":["https://openalex.org/I123443094"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Yandre M. G. Costa","raw_affiliation_strings":["State University of Maringa (UEM), Maringa, Parana, Brazil"],"affiliations":[{"raw_affiliation_string":"State University of Maringa (UEM), Maringa, Parana, Brazil","institution_ids":["https://openalex.org/I123443094"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084140678","display_name":"George D. C. Cavalcanti","orcid":"https://orcid.org/0000-0001-7714-2283"},"institutions":[{"id":"https://openalex.org/I25112270","display_name":"Universidade Federal de Pernambuco","ror":"https://ror.org/047908t24","country_code":"BR","type":"education","lineage":["https://openalex.org/I25112270"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"George D. C. Cavalcanti","raw_affiliation_strings":["Federal University of Pernambuco (UFPE), Recife, Pernambuco, Brazil"],"affiliations":[{"raw_affiliation_string":"Federal University of Pernambuco (UFPE), Recife, Pernambuco, Brazil","institution_ids":["https://openalex.org/I25112270"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112404986","display_name":"Alceu S. Britto","orcid":null},"institutions":[{"id":"https://openalex.org/I176838256","display_name":"Pontif\u00edcia Universidade Cat\u00f3lica do Paran\u00e1","ror":"https://ror.org/02x1vjk79","country_code":"BR","type":"education","lineage":["https://openalex.org/I176838256"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Alceu S. Britto","raw_affiliation_strings":["Pontifical Catholic University of Paran\u0101 (PUCPR), Curitiba, Paran\u0101, Brazil"],"affiliations":[{"raw_affiliation_string":"Pontifical Catholic University of Paran\u0101 (PUCPR), Curitiba, Paran\u0101, Brazil","institution_ids":["https://openalex.org/I176838256"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5038884704","display_name":"Luiz S. Oliveira","orcid":"https://orcid.org/0000-0002-0595-5370"},"institutions":[{"id":"https://openalex.org/I52418104","display_name":"Universidade Federal do Paran\u00e1","ror":"https://ror.org/05syd6y78","country_code":"BR","type":"education","lineage":["https://openalex.org/I52418104"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Luiz E. S. Oliveira","raw_affiliation_strings":["Federal University of Paran\u00e1 (UFPR), Curitiba, Paran\u00e1, Brazil"],"affiliations":[{"raw_affiliation_string":"Federal University of Paran\u00e1 (UFPR), Curitiba, Paran\u00e1, Brazil","institution_ids":["https://openalex.org/I52418104"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5070687435"],"corresponding_institution_ids":["https://openalex.org/I123443094"],"apc_list":null,"apc_paid":null,"fwci":0.1012,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.42921492,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"63","last_page":"68"},"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":0.9894000291824341,"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/T12357","display_name":"Digital Media Forensic Detection","score":0.9882000088691711,"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":0.801784873008728},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.7950541377067566},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7462953925132751},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7355514764785767},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6080091595649719},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5886339545249939},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5512007474899292},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5006518363952637},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4917071461677551},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.4607009291648865},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.42365992069244385},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.34453219175338745}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.801784873008728},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.7950541377067566},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7462953925132751},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7355514764785767},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6080091595649719},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5886339545249939},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5512007474899292},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5006518363952637},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4917071461677551},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.4607009291648865},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.42365992069244385},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.34453219175338745},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"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/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iwssip.2019.8787293","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iwssip.2019.8787293","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Conference on Systems, Signals and Image Processing (IWSSIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.8299999833106995}],"awards":[],"funders":[{"id":"https://openalex.org/F4320322025","display_name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico","ror":"https://ror.org/03swz6y49"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W1507294259","https://openalex.org/W1546044829","https://openalex.org/W1606323814","https://openalex.org/W1687753945","https://openalex.org/W1983010777","https://openalex.org/W2047791693","https://openalex.org/W2048649700","https://openalex.org/W2050995497","https://openalex.org/W2088120514","https://openalex.org/W2105065129","https://openalex.org/W2120888827","https://openalex.org/W2124735751","https://openalex.org/W2130478883","https://openalex.org/W2133059825","https://openalex.org/W2152928267","https://openalex.org/W2157298821","https://openalex.org/W2158275940","https://openalex.org/W2163922914","https://openalex.org/W2187089797","https://openalex.org/W2235604683","https://openalex.org/W2520933715","https://openalex.org/W2574589360","https://openalex.org/W2883949996","https://openalex.org/W2884711366","https://openalex.org/W2964292554","https://openalex.org/W2999778248","https://openalex.org/W4285719527","https://openalex.org/W4393825830","https://openalex.org/W6630451936","https://openalex.org/W6637477795","https://openalex.org/W6680096826","https://openalex.org/W6689470822","https://openalex.org/W6726776450","https://openalex.org/W6753219256","https://openalex.org/W6753794970","https://openalex.org/W6858072613"],"related_works":["https://openalex.org/W4293226380","https://openalex.org/W2090763504","https://openalex.org/W148178222","https://openalex.org/W2104657898","https://openalex.org/W1948992892","https://openalex.org/W1886884218","https://openalex.org/W1910826599","https://openalex.org/W2770593030","https://openalex.org/W2012353789","https://openalex.org/W1996690921"],"abstract_inverted_index":{"Writer":[0],"identification":[1,74,91],"by":[2,75,135],"using":[3,76,136,150,169,176,185],"manuscripts":[4],"became":[5],"a":[6,29,80,107,137],"very":[7],"important":[8],"research":[9,41],"topic":[10],"in":[11,43,69,215],"forensic":[12],"analysis":[13],"of":[14,28,46,53,65,72,99,122,166,203],"documents.":[15,145],"That":[16],"is":[17],"because":[18],"the":[19,33,36,40,63,70,89,97,114,120,143,153,157,164,167,177,186,193,201,204,216],"writing":[20],"can":[21,49],"be":[22],"considered":[23],"as":[24],"an":[25],"identifying":[26],"characteristic":[27],"person.":[30],"By":[31],"analyzing":[32],"challenges":[34],"and":[35,126,189],"resources":[37],"available":[38],"for":[39],"development":[42],"this":[44,57,123],"field":[45],"investigation,":[47],"one":[48],"find":[50],"different":[51],"databases":[52,128],"writers":[54],"manuscripts.":[55,77,158],"In":[56],"work,":[58],"we":[59,160],"aim":[60],"to":[61,87,106,212],"evaluate":[62],"performance":[64,98,199,208],"deep":[66],"learning":[67,147],"techniques":[68],"process":[71],"writer":[73,90],"For":[78],"this,":[79],"convolutional":[81],"neural":[82],"network":[83],"(CNN)":[84],"was":[85,148],"developed":[86],"address":[88],"task.":[92],"We":[93],"have":[94,161],"also":[95,162],"evaluated":[96,163],"features":[100,194],"obtained":[101,155,195],"with":[102,173,196,207],"CNN":[103,151],"when":[104],"submitted":[105],"support":[108],"vector":[109],"machine":[110],"(SVM)":[111],"classifier,":[112],"considering":[113],"traditional":[115],"classification":[116,168,171],"approach.":[117,179],"To":[118],"assert":[119],"objectives":[121],"proposal,":[124],"CVL":[125],"BFL":[127],"were":[129,133,183],"used.":[130],"The":[131,180,198],"experiments":[132],"conducted":[134],"texture":[138,154],"generation":[139],"approach,":[140],"starting":[141],"from":[142,156],"original":[144],"Feature":[146],"accomplished":[149],"on":[152,192],"Finally,":[159],"impact":[165],"both":[170],"scenarios":[172],"or":[174,210],"without":[175],"dissimilarity":[178,190],"best":[181],"results":[182],"achieved":[184],"SVM":[187],"classifier":[188],"approach":[191],"CNN.":[197],"demonstrated":[200],"robustness":[202],"proposed":[205],"method":[206],"similar":[209],"superior":[211],"that":[213],"described":[214],"literature.":[217]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"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"}
