{"id":"https://openalex.org/W2563209596","doi":"https://doi.org/10.1109/dicta.2016.7797031","title":"Deep Neural Networks for Page Stream Segmentation and Classification","display_name":"Deep Neural Networks for Page Stream Segmentation and Classification","publication_year":2016,"publication_date":"2016-11-01","ids":{"openalex":"https://openalex.org/W2563209596","doi":"https://doi.org/10.1109/dicta.2016.7797031","mag":"2563209596"},"language":"en","primary_location":{"id":"doi:10.1109/dicta.2016.7797031","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dicta.2016.7797031","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA)","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/A5054778549","display_name":"Ignazio Gallo","orcid":"https://orcid.org/0000-0002-7076-8328"},"institutions":[{"id":"https://openalex.org/I115752224","display_name":"University of Insubria","ror":"https://ror.org/00s409261","country_code":"IT","type":"education","lineage":["https://openalex.org/I115752224"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Ignazio Gallo","raw_affiliation_strings":["Dept. of Theor. &amp; Appl. Sci., Univ. of Insubria, Varese, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dept. of Theor. &amp; Appl. Sci., Univ. of Insubria, Varese, Italy","institution_ids":["https://openalex.org/I115752224"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108581053","display_name":"Lucia Noce","orcid":null},"institutions":[{"id":"https://openalex.org/I115752224","display_name":"University of Insubria","ror":"https://ror.org/00s409261","country_code":"IT","type":"education","lineage":["https://openalex.org/I115752224"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Lucia Noce","raw_affiliation_strings":["Dept. of Theor. &amp; Appl. Sci., Univ. of Insubria, Varese, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dept. of Theor. &amp; Appl. Sci., Univ. of Insubria, Varese, Italy","institution_ids":["https://openalex.org/I115752224"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081791308","display_name":"Alessandro Zamberletti","orcid":null},"institutions":[{"id":"https://openalex.org/I115752224","display_name":"University of Insubria","ror":"https://ror.org/00s409261","country_code":"IT","type":"education","lineage":["https://openalex.org/I115752224"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Alessandro Zamberletti","raw_affiliation_strings":["Dept. of Theor. &amp; Appl. Sci., Univ. of Insubria, Varese, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dept. of Theor. &amp; Appl. Sci., Univ. of Insubria, Varese, Italy","institution_ids":["https://openalex.org/I115752224"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5087859719","display_name":"Alessandro Calefati","orcid":"https://orcid.org/0000-0003-3860-4785"},"institutions":[{"id":"https://openalex.org/I115752224","display_name":"University of Insubria","ror":"https://ror.org/00s409261","country_code":"IT","type":"education","lineage":["https://openalex.org/I115752224"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Alessandro Calefati","raw_affiliation_strings":["Dept. of Theor. &amp; Appl. Sci., Univ. of Insubria, Varese, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dept. of Theor. &amp; Appl. Sci., Univ. of Insubria, Varese, Italy","institution_ids":["https://openalex.org/I115752224"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I115752224"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10601","display_name":"Handwritten Text Recognition Techniques","score":0.996399998664856,"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":0.996399998664856,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.9962999820709229,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T14319","display_name":"Currency Recognition and Detection","score":0.9947999715805054,"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":0.857322096824646},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.7330987453460693},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6637157797813416},{"id":"https://openalex.org/keywords/sliding-window-protocol","display_name":"Sliding window protocol","score":0.6445099115371704},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.619765043258667},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5939319729804993},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5423875451087952},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.5412899851799011},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.535170316696167},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4835219383239746},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4448016881942749},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.37476590275764465},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.32507243752479553},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.30573177337646484},{"id":"https://openalex.org/keywords/window","display_name":"Window (computing)","score":0.29789045453071594},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.07602930068969727}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.857322096824646},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.7330987453460693},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6637157797813416},{"id":"https://openalex.org/C102392041","wikidata":"https://www.wikidata.org/wiki/Q592860","display_name":"Sliding window protocol","level":3,"score":0.6445099115371704},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.619765043258667},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5939319729804993},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5423875451087952},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.5412899851799011},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.535170316696167},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4835219383239746},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4448016881942749},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.37476590275764465},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.32507243752479553},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.30573177337646484},{"id":"https://openalex.org/C2778751112","wikidata":"https://www.wikidata.org/wiki/Q835016","display_name":"Window (computing)","level":2,"score":0.29789045453071594},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.07602930068969727},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/dicta.2016.7797031","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dicta.2016.7797031","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA)","raw_type":"proceedings-article"},{"id":"pmh:oai:irinsubria.uninsubria.it:11383/2063096","is_oa":false,"landing_page_url":"http://hdl.handle.net/11383/2063096","pdf_url":null,"source":{"id":"https://openalex.org/S4377196351","display_name":"IrInSubria (University of Insubria)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I115752224","host_organization_name":"University of Insubria","host_organization_lineage":["https://openalex.org/I115752224"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5400000214576721,"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320309480","display_name":"Nvidia","ror":"https://ror.org/03jdj4y14"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W183625566","https://openalex.org/W1501373171","https://openalex.org/W1665214252","https://openalex.org/W1977813952","https://openalex.org/W2028571532","https://openalex.org/W2031408949","https://openalex.org/W2083122709","https://openalex.org/W2097624458","https://openalex.org/W2098345386","https://openalex.org/W2109902731","https://openalex.org/W2133990480","https://openalex.org/W2155893237","https://openalex.org/W2163605009","https://openalex.org/W2490662969","https://openalex.org/W2529099292","https://openalex.org/W2560674852","https://openalex.org/W2618530766","https://openalex.org/W2962772269","https://openalex.org/W6637242042"],"related_works":["https://openalex.org/W4299822940","https://openalex.org/W2279398222","https://openalex.org/W2766604260","https://openalex.org/W2986507176","https://openalex.org/W4366492315","https://openalex.org/W2946016983","https://openalex.org/W3160711233","https://openalex.org/W4220996320","https://openalex.org/W3156786002","https://openalex.org/W2738461075"],"abstract_inverted_index":{"In":[0],"this":[1],"manuscript":[2],"we":[3,54],"propose":[4],"a":[5,22,64,110,133,142],"novel":[6],"method":[7],"for":[8],"jointly":[9],"page":[10],"stream":[11,23],"segmentation":[12],"and":[13,53,63,117,156],"multi-page":[14,144],"document":[15,50],"classification.The":[16],"end":[17],"goal":[18],"is":[19,70,82],"to":[20,28,58,114],"classify":[21],"of":[24,31,36,75,94,138],"pages":[25],"as":[26,49],"belonging":[27],"different":[29],"classes":[30],"documents.":[32],"We":[33],"take":[34],"advantage":[35],"the":[37,73,79,86,92,95,123],"recent":[38],"state-of-the-art":[39],"results":[40,155],"achieved":[41],"using":[42,101],"deep":[43,104],"architectures":[44],"in":[45,109],"related":[46],"fields":[47],"such":[48],"image":[51],"classification,":[52],"adopt":[55],"similar":[56],"models":[57],"obtain":[59],"satisfying":[60],"classification":[61,119],"accuracies":[62],"low":[65],"computational":[66],"complexity.":[67],"Our":[68],"contribution":[69],"twofold:":[71],"first,":[72],"extraction":[74],"visual":[76],"features":[77],"from":[78],"processed":[80],"documents":[81,145],"automatically":[83],"performed":[84],"by":[85,122,147],"chosen":[87],"Convolutional":[88],"Neural":[89],"Network;":[90],"second,":[91],"predictions":[93],"same":[96],"network":[97],"are":[98],"further":[99],"refined":[100],"an":[102,148],"additional":[103],"model":[105],"which":[106],"processes":[107],"them":[108],"classic":[111],"sliding-window":[112],"manner":[113],"help":[115],"finding":[116],"solving":[118],"errors":[120],"committed":[121],"first":[124],"network.":[125],"The":[126],"proposed":[127],"pipeline":[128],"has":[129],"been":[130],"evaluated":[131],"on":[132],"publicly":[134],"available":[135],"dataset":[136],"composed":[137],"more":[139],"than":[140],"half":[141],"million":[143],"collected":[146],"on-line":[149],"loan":[150],"comparison":[151],"company,":[152],"showing":[153],"excellent":[154],"high":[157],"efficiency.":[158]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":4},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":1}],"updated_date":"2026-07-15T18:14:33.161393","created_date":"2025-10-10T00:00:00"}
