{"id":"https://openalex.org/W2981126613","doi":"https://doi.org/10.1145/3352631.3352639","title":"Using Balanced Training to Minimize Biased Classification","display_name":"Using Balanced Training to Minimize Biased Classification","publication_year":2019,"publication_date":"2019-09-20","ids":{"openalex":"https://openalex.org/W2981126613","doi":"https://doi.org/10.1145/3352631.3352639","mag":"2981126613"},"language":"en","primary_location":{"id":"doi:10.1145/3352631.3352639","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3352631.3352639","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 5th International Workshop on Historical Document Imaging and Processing","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":null,"display_name":"Redy Andriyansah","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Redy Andriyansah","raw_affiliation_strings":["Ministry of Finance Indonesia, Jakarta, Indonesia"],"affiliations":[{"raw_affiliation_string":"Ministry of Finance Indonesia, Jakarta, Indonesia","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112334801","display_name":"Syed Saqib Bukhari","orcid":null},"institutions":[{"id":"https://openalex.org/I33256026","display_name":"German Research Centre for Artificial Intelligence","ror":"https://ror.org/01ayc5b57","country_code":"DE","type":"funder","lineage":["https://openalex.org/I33256026"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Syed Saqib Bukhari","raw_affiliation_strings":["German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany"],"affiliations":[{"raw_affiliation_string":"German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany","institution_ids":["https://openalex.org/I33256026"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079499227","display_name":"Martin Jenckel","orcid":null},"institutions":[{"id":"https://openalex.org/I33256026","display_name":"German Research Centre for Artificial Intelligence","ror":"https://ror.org/01ayc5b57","country_code":"DE","type":"funder","lineage":["https://openalex.org/I33256026"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Martin Jenckel","raw_affiliation_strings":["German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany"],"affiliations":[{"raw_affiliation_string":"German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany","institution_ids":["https://openalex.org/I33256026"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101904182","display_name":"Andreas Dengel","orcid":"https://orcid.org/0000-0002-6100-8255"},"institutions":[{"id":"https://openalex.org/I33256026","display_name":"German Research Centre for Artificial Intelligence","ror":"https://ror.org/01ayc5b57","country_code":"DE","type":"funder","lineage":["https://openalex.org/I33256026"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Andreas Dengel","raw_affiliation_strings":["Department of Computer Science TU Kaiserslautern, German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science TU Kaiserslautern, German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany","institution_ids":["https://openalex.org/I33256026"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.11575928,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"31","last_page":"36"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10601","display_name":"Handwritten Text Recognition Techniques","score":0.9970999956130981,"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":0.9970999956130981,"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":0.9922999739646912,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9878000020980835,"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":0.8184259533882141},{"id":"https://openalex.org/keywords/ambiguity","display_name":"Ambiguity","score":0.665460467338562},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6447595357894897},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6066452264785767},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.5473783016204834},{"id":"https://openalex.org/keywords/heuristic","display_name":"Heuristic","score":0.46316516399383545},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.4552707374095917},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4247836470603943},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.4184810519218445},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4132123589515686},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.39203721284866333},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3574124276638031}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8184259533882141},{"id":"https://openalex.org/C2780522230","wikidata":"https://www.wikidata.org/wiki/Q1140419","display_name":"Ambiguity","level":2,"score":0.665460467338562},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6447595357894897},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6066452264785767},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.5473783016204834},{"id":"https://openalex.org/C173801870","wikidata":"https://www.wikidata.org/wiki/Q201413","display_name":"Heuristic","level":2,"score":0.46316516399383545},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.4552707374095917},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4247836470603943},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.4184810519218445},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4132123589515686},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.39203721284866333},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3574124276638031},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3352631.3352639","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3352631.3352639","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 5th International Workshop on Historical Document Imaging and Processing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.550000011920929,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W171088530","https://openalex.org/W205301110","https://openalex.org/W592081968","https://openalex.org/W1444003893","https://openalex.org/W1510710711","https://openalex.org/W2016626577","https://openalex.org/W2028571532","https://openalex.org/W2105767494","https://openalex.org/W2148143831","https://openalex.org/W2163605009","https://openalex.org/W2597501072","https://openalex.org/W2598912124","https://openalex.org/W2604272474","https://openalex.org/W2605976347","https://openalex.org/W2962772269","https://openalex.org/W6681151457"],"related_works":["https://openalex.org/W2353179089","https://openalex.org/W2923538289","https://openalex.org/W2353125546","https://openalex.org/W2470643824","https://openalex.org/W4400595174","https://openalex.org/W2349635380","https://openalex.org/W4353089801","https://openalex.org/W2353819554","https://openalex.org/W2359488321","https://openalex.org/W2389866386"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3,107,126,138],"classify":[4,67],"semantic":[5,32,68],"zone":[6,33,69,102],"in":[7,27,49],"a":[8,14,63,75],"document":[9,23,92,132],"image":[10],"and":[11,29,95,111,148],"observe":[12],"how":[13],"balanced":[15,147,155],"training":[16,57,114,156],"influences":[17],"the":[18,109,113],"classification":[19],"performance.":[20,160],"Unlike":[21],"holistic":[22],"which":[24,73],"normally":[25],"distinguishes":[26],"content":[28],"structural":[30],"layout,":[31],"introduces":[34],"stronger":[35],"inter-class":[36],"ambiguity":[37],"as":[38],"it":[39],"loses":[40],"layout":[41],"feature.":[42],"Zone":[43],"extraction":[44],"from":[45,130],"documents":[46],"often":[47],"results":[48],"unbalanced":[50,149],"class":[51],"distribution.":[52],"Our":[53],"experiment":[54],"shows":[55,153],"that":[56,154],"on":[58,82],"such":[59],"data":[60,99,110],"leads":[61],"to":[62,116,143],"biased":[64,159],"classification.":[65],"We":[66],"by":[70,120],"using":[71],"AlexNet":[72],"is":[74,104],"Convolutional":[76],"Neural":[77],"Network":[78],"(CNN).":[79],"It":[80],"works":[81],"3":[83],"corpora:":[84],"University":[85],"of":[86,97],"Washington":[87],"(UW)":[88],"III,":[89],"German":[90],"historical":[91],"images":[93],"(OCRD),":[94],"combination":[96],"both":[98],"sets.":[100],"Because":[101],"distribution":[103,115],"heavily":[105],"unbalanced,":[106],"augment":[108],"balance":[112],"prevent":[117],"over":[118],"expression":[119],"major":[121],"classes.":[122],"To":[123],"maintain":[124],"accuracy,":[125],"adopt":[127],"transfer":[128],"learning":[129],"larger":[131],"corpus":[133],"(RVLCDIP).":[134],"Besides":[135],"deep":[136],"learning,":[137],"also":[139],"use":[140],"heuristic":[141],"approach":[142],"compare":[144],"performance":[145],"between":[146],"training.":[150],"The":[151],"result":[152],"can":[157],"alleviate":[158]},"counts_by_year":[],"updated_date":"2026-04-17T18:11:37.981687","created_date":"2025-10-10T00:00:00"}
