{"id":"https://openalex.org/W4312536607","doi":"https://doi.org/10.1109/icpr56361.2022.9956167","title":"Are Deep Models Robust against Real Distortions? A Case Study on Document Image Classification","display_name":"Are Deep Models Robust against Real Distortions? A Case Study on Document Image Classification","publication_year":2022,"publication_date":"2022-08-21","ids":{"openalex":"https://openalex.org/W4312536607","doi":"https://doi.org/10.1109/icpr56361.2022.9956167"},"language":"en","primary_location":{"id":"doi:10.1109/icpr56361.2022.9956167","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr56361.2022.9956167","pdf_url":null,"source":{"id":"https://openalex.org/S4363607731","display_name":"2022 26th International Conference on Pattern Recognition (ICPR)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 26th International Conference on Pattern Recognition (ICPR)","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/A5107070776","display_name":"Saifullah","orcid":null},"institutions":[{"id":"https://openalex.org/I153267046","display_name":"University of Kaiserslautern","ror":"https://ror.org/04zrf7b53","country_code":"DE","type":"education","lineage":["https://openalex.org/I153267046"]},{"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":true,"raw_author_name":"Saifullah","raw_affiliation_strings":["German Research Center for Artificial Intelligence (DFKI),Kaiserslautern,Germany,67663","TU Kaiserslautern, Kaiserslautern, Germany"],"affiliations":[{"raw_affiliation_string":"German Research Center for Artificial Intelligence (DFKI),Kaiserslautern,Germany,67663","institution_ids":["https://openalex.org/I33256026"]},{"raw_affiliation_string":"TU Kaiserslautern, Kaiserslautern, Germany","institution_ids":["https://openalex.org/I153267046"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053635416","display_name":"Shoaib Ahmed Siddiqui","orcid":"https://orcid.org/0000-0003-4600-7331"},"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":"Shoaib Ahmed Siddiqui","raw_affiliation_strings":["German Research Center for Artificial Intelligence (DFKI),Kaiserslautern,Germany,67663"],"affiliations":[{"raw_affiliation_string":"German Research Center for Artificial Intelligence (DFKI),Kaiserslautern,Germany,67663","institution_ids":["https://openalex.org/I33256026"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083272976","display_name":"Stefan Agne","orcid":"https://orcid.org/0000-0002-9697-4285"},"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":"Stefan Agne","raw_affiliation_strings":["German Research Center for Artificial Intelligence (DFKI),Kaiserslautern,Germany,67663","DeepReader GmbH, Kaiserlautern, Germany"],"affiliations":[{"raw_affiliation_string":"German Research Center for Artificial Intelligence (DFKI),Kaiserslautern,Germany,67663","institution_ids":["https://openalex.org/I33256026"]},{"raw_affiliation_string":"DeepReader GmbH, Kaiserlautern, Germany","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101904182","display_name":"Andreas Dengel","orcid":"https://orcid.org/0000-0002-6100-8255"},"institutions":[{"id":"https://openalex.org/I153267046","display_name":"University of Kaiserslautern","ror":"https://ror.org/04zrf7b53","country_code":"DE","type":"education","lineage":["https://openalex.org/I153267046"]},{"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":["German Research Center for Artificial Intelligence (DFKI),Kaiserslautern,Germany,67663","TU Kaiserslautern, Kaiserslautern, Germany"],"affiliations":[{"raw_affiliation_string":"German Research Center for Artificial Intelligence (DFKI),Kaiserslautern,Germany,67663","institution_ids":["https://openalex.org/I33256026"]},{"raw_affiliation_string":"TU Kaiserslautern, Kaiserslautern, Germany","institution_ids":["https://openalex.org/I153267046"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5058813180","display_name":"Sheraz Ahmed","orcid":"https://orcid.org/0000-0002-4239-6520"},"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":"Sheraz Ahmed","raw_affiliation_strings":["German Research Center for Artificial Intelligence (DFKI),Kaiserslautern,Germany,67663","DeepReader GmbH, Kaiserlautern, Germany"],"affiliations":[{"raw_affiliation_string":"German Research Center for Artificial Intelligence (DFKI),Kaiserslautern,Germany,67663","institution_ids":["https://openalex.org/I33256026"]},{"raw_affiliation_string":"DeepReader GmbH, Kaiserlautern, Germany","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5107070776"],"corresponding_institution_ids":["https://openalex.org/I153267046","https://openalex.org/I33256026"],"apc_list":null,"apc_paid":null,"fwci":0.5397,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.75481249,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1628","last_page":"1635"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10601","display_name":"Handwritten Text Recognition Techniques","score":0.9954000115394592,"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":0.9954000115394592,"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":0.9934999942779541,"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/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.9775999784469604,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.8571068048477173},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7591646909713745},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.7029346823692322},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6531893610954285},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.5522581338882446},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5519921183586121},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.48489123582839966},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4779679477214813},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.38044285774230957},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.35425329208374023}],"concepts":[{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.8571068048477173},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7591646909713745},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.7029346823692322},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6531893610954285},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.5522581338882446},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5519921183586121},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.48489123582839966},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4779679477214813},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.38044285774230957},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.35425329208374023},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"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/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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icpr56361.2022.9956167","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr56361.2022.9956167","pdf_url":null,"source":{"id":"https://openalex.org/S4363607731","display_name":"2022 26th International Conference on Pattern Recognition (ICPR)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 26th International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","score":0.8500000238418579,"display_name":"Good health and well-being"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":57,"referenced_works":["https://openalex.org/W205301110","https://openalex.org/W1510710711","https://openalex.org/W1686810756","https://openalex.org/W1731296920","https://openalex.org/W1945616565","https://openalex.org/W2028571532","https://openalex.org/W2098345386","https://openalex.org/W2117539524","https://openalex.org/W2119755031","https://openalex.org/W2134533274","https://openalex.org/W2143612262","https://openalex.org/W2156332201","https://openalex.org/W2163605009","https://openalex.org/W2194775991","https://openalex.org/W2605976347","https://openalex.org/W2612573399","https://openalex.org/W2765424254","https://openalex.org/W2896457183","https://openalex.org/W2912237282","https://openalex.org/W2953937638","https://openalex.org/W2954540134","https://openalex.org/W2955425717","https://openalex.org/W2962772269","https://openalex.org/W2963178695","https://openalex.org/W2963336383","https://openalex.org/W2964036463","https://openalex.org/W2968993450","https://openalex.org/W2994088087","https://openalex.org/W2997154779","https://openalex.org/W3013022628","https://openalex.org/W3035251701","https://openalex.org/W3094502228","https://openalex.org/W3101483505","https://openalex.org/W3103557498","https://openalex.org/W3104953317","https://openalex.org/W3176851559","https://openalex.org/W3198902415","https://openalex.org/W4288404646","https://openalex.org/W4297812480","https://openalex.org/W4308831279","https://openalex.org/W6608326786","https://openalex.org/W6630471269","https://openalex.org/W6637373629","https://openalex.org/W6637855997","https://openalex.org/W6640425456","https://openalex.org/W6684191040","https://openalex.org/W6740417705","https://openalex.org/W6755207826","https://openalex.org/W6756444276","https://openalex.org/W6757555829","https://openalex.org/W6758930985","https://openalex.org/W6762718338","https://openalex.org/W6764990469","https://openalex.org/W6765696844","https://openalex.org/W6770979763","https://openalex.org/W6784333009","https://openalex.org/W6987838976"],"related_works":["https://openalex.org/W2378211422","https://openalex.org/W2745001401","https://openalex.org/W4321353415","https://openalex.org/W2130974462","https://openalex.org/W972276598","https://openalex.org/W4246352526","https://openalex.org/W2028665553","https://openalex.org/W2086519370","https://openalex.org/W2087343574","https://openalex.org/W2121910908"],"abstract_inverted_index":{"As":[0],"deep":[1],"learning":[2],"models":[3,111,144],"in":[4,46,135,153,162,183],"the":[5,29,47,67,89,95,149,154,159,189],"context":[6],"of":[7,31,40,81,98,140,156,181],"document":[8,34,70,100,184],"image":[9,35,71,101,185],"classification":[10,72,102,125,186],"are":[11,22,43,63],"reaching":[12,166],"diminishing":[13],"returns":[14],"with":[15,77,124,158],"nearperfect":[16],"recognition":[17],"scores,":[18],"their":[19,163],"robustness":[20,30,96],"characteristics":[21,97],"poorly":[23],"understood.":[24],"In":[25],"order":[26],"to":[27,93,130,175,197],"evaluate":[28],"existing":[32,99],"state-of-the-art":[33],"classifiers":[36],"against":[37],"different":[38,79],"types":[39,80],"distortions":[41,82],"that":[42,107],"commonly":[44],"encountered":[45],"real":[48],"world,":[49],"we":[50],"present":[51],"two":[52],"separate":[53],"benchmark":[54,91],"datasets,":[55],"namely":[56],"RVL-CDIPD":[57],"and":[58,75,195],"Tobacco3482-D.":[59],"The":[60,193],"proposed":[61,90],"benchmarks":[62,174],"generated":[64],"by":[65],"augmenting":[66],"well-known":[68],"pre-existing":[69],"datasets":[73,92,194],"(RVL-CDIP":[74],"Tobacco3482)":[76],"21":[78],"including":[83],"varying":[84],"severity":[85],"levels.":[86],"We":[87,171],"leverage":[88],"analyze":[94],"systems.":[103],"Our":[104],"analysis":[105],"reveals":[106],"despite":[108],"higher":[109,114],"accuracy":[110,143,164,191],"exhibiting":[112],"relatively":[113],"robustness,":[115],"they":[116],"still":[117],"severely":[118],"underperform":[119],"on":[120],"some":[121,136,139],"specific":[122],"distortions,":[123,157],"accuracies":[126],"dropping":[127],"from":[128],"~90%":[129],"as":[131,133,167,169,177],"low":[132],"~40%":[134],"cases.":[137],"Interestingly,":[138],"these":[141,173],"high":[142,168],"perform":[145],"even":[146],"worse":[147],"than":[148],"baseline":[150],"AlexNet":[151],"model":[152],"presence":[155],"relative":[160],"decline":[161],"sometimes":[165],"300-450%.":[170],"envision":[172],"serve":[176],"a":[178],"strong":[179],"signal":[180],"progress":[182],"tasks,":[187],"beyond":[188],"saturated":[190],"metrics.":[192],"code":[196],"reproduce":[198],"them":[199],"is":[200],"publicly":[201],"available:":[202],"https://github.com/saifullah3396/docrobustness.":[203]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}