{"id":"https://openalex.org/W2965085721","doi":"https://doi.org/10.24963/ijcai.2019/466","title":"Deterministic Routing between Layout Abstractions for Multi-Scale Classification of Visually Rich Documents","display_name":"Deterministic Routing between Layout Abstractions for Multi-Scale Classification of Visually Rich Documents","publication_year":2019,"publication_date":"2019-07-28","ids":{"openalex":"https://openalex.org/W2965085721","doi":"https://doi.org/10.24963/ijcai.2019/466","mag":"2965085721"},"language":"en","primary_location":{"id":"doi:10.24963/ijcai.2019/466","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2019/466","pdf_url":"https://www.ijcai.org/proceedings/2019/0466.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.ijcai.org/proceedings/2019/0466.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5014228532","display_name":"Ritesh Sarkhel","orcid":"https://orcid.org/0000-0002-9116-1517"},"institutions":[{"id":"https://openalex.org/I52357470","display_name":"The Ohio State University","ror":"https://ror.org/00rs6vg23","country_code":"US","type":"education","lineage":["https://openalex.org/I52357470"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ritesh Sarkhel","raw_affiliation_strings":["Department of Computer Science and Engineering, The Ohio State University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, The Ohio State University","institution_ids":["https://openalex.org/I52357470"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5001906560","display_name":"Arnab Nandi","orcid":"https://orcid.org/0000-0002-4138-603X"},"institutions":[{"id":"https://openalex.org/I52357470","display_name":"The Ohio State University","ror":"https://ror.org/00rs6vg23","country_code":"US","type":"education","lineage":["https://openalex.org/I52357470"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Arnab Nandi","raw_affiliation_strings":["Department of Computer Science and Engineering, The Ohio State University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, The Ohio State University","institution_ids":["https://openalex.org/I52357470"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I52357470"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":32,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"3360","last_page":"3366"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9994000196456909,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9994000196456909,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9980999827384949,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.993399977684021,"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":0.8128237724304199},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.7796144485473633},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.657324492931366},{"id":"https://openalex.org/keywords/pyramid","display_name":"Pyramid (geometry)","score":0.6370319128036499},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.6249108910560608},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.6149643659591675},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5478205680847168},{"id":"https://openalex.org/keywords/scheme","display_name":"Scheme (mathematics)","score":0.507691502571106},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.499126672744751},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.48590603470802307},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4666217565536499},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4645988643169403},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.4404796063899994},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4343033730983734},{"id":"https://openalex.org/keywords/routing","display_name":"Routing (electronic design automation)","score":0.4272669553756714},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4164772629737854},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3645137548446655}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8128237724304199},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.7796144485473633},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.657324492931366},{"id":"https://openalex.org/C142575187","wikidata":"https://www.wikidata.org/wiki/Q3358290","display_name":"Pyramid (geometry)","level":2,"score":0.6370319128036499},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.6249108910560608},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6149643659591675},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5478205680847168},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.507691502571106},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.499126672744751},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.48590603470802307},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4666217565536499},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4645988643169403},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.4404796063899994},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4343033730983734},{"id":"https://openalex.org/C74172769","wikidata":"https://www.wikidata.org/wiki/Q1446839","display_name":"Routing (electronic design automation)","level":2,"score":0.4272669553756714},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4164772629737854},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3645137548446655},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"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/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.24963/ijcai.2019/466","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2019/466","pdf_url":"https://www.ijcai.org/proceedings/2019/0466.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.24963/ijcai.2019/466","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2019/466","pdf_url":"https://www.ijcai.org/proceedings/2019/0466.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.7300000190734863,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2965085721.pdf","grobid_xml":"https://content.openalex.org/works/W2965085721.grobid-xml"},"referenced_works_count":24,"referenced_works":["https://openalex.org/W1587119849","https://openalex.org/W1677409904","https://openalex.org/W1966382373","https://openalex.org/W2001642682","https://openalex.org/W2004665841","https://openalex.org/W2062310461","https://openalex.org/W2078294512","https://openalex.org/W2082163619","https://openalex.org/W2099937879","https://openalex.org/W2130746998","https://openalex.org/W2151103935","https://openalex.org/W2156332201","https://openalex.org/W2162915993","https://openalex.org/W2165698076","https://openalex.org/W2338128728","https://openalex.org/W2522010881","https://openalex.org/W2620281313","https://openalex.org/W2788993238","https://openalex.org/W2808606449","https://openalex.org/W2884561390","https://openalex.org/W2962772269","https://openalex.org/W2963163009","https://openalex.org/W2963336383","https://openalex.org/W4297775537"],"related_works":["https://openalex.org/W2965546495","https://openalex.org/W4389116644","https://openalex.org/W2153315159","https://openalex.org/W3103844505","https://openalex.org/W259157601","https://openalex.org/W4205463238","https://openalex.org/W2761785940","https://openalex.org/W2110523656","https://openalex.org/W1482209366","https://openalex.org/W2797752778"],"abstract_inverted_index":{"Classifying":[0],"heterogeneous":[1],"visually":[2,74,126],"rich":[3,75,127],"documents":[4],"is":[5,23,108],"a":[6,26,61,73,88],"challenging":[7],"task.":[8],"Difficulty":[9],"of":[10,55,82,125],"this":[11,56],"task":[12],"increases":[13],"even":[14],"more":[15],"if":[16],"the":[17,36,79,98,116,140],"maximum":[18],"allowed":[19],"inference":[20,32,95,149],"turnaround":[21],"time":[22],"constrained":[24],"by":[25,77,96],"threshold.":[27],"The":[28],"increased":[29],"overhead":[30],"in":[31,39,47,143],"cost,":[33],"compared":[34,138],"to":[35,65,110,139],"limited":[37],"gain":[38],"classification":[40,145],"capabilities":[41],"make":[42],"current":[43],"multi-scale":[44,69],"approaches":[45],"infeasible":[46],"such":[48],"scenarios.":[49],"There":[50],"are":[51],"two":[52],"major":[53],"contributions":[54],"work.":[57],"First,":[58],"we":[59,86],"propose":[60,87],"spatial":[62,99],"pyramid":[63,100],"model":[64],"extract":[66],"highly":[67],"discriminative":[68],"feature":[70],"descriptors":[71],"from":[72],"document":[76],"leveraging":[78],"inherent":[80],"hierarchy":[81],"its":[83],"layout.":[84],"Second,":[85],"deterministic":[89],"routing":[90],"scheme":[91],"for":[92],"accelerating":[93],"end-to-end":[94],"utilizing":[97],"model.":[101],"A":[102],"depth-wise":[103],"separable":[104],"multi-column":[105],"convolutional":[106],"network":[107],"developed":[109],"enable":[111],"our":[112,132],"method.":[113],"We":[114],"evaluated":[115],"proposed":[117,133],"approach":[118,134],"on":[119],"four":[120],"publicly":[121],"available,":[122],"benchmark":[123],"datasets":[124],"documents.":[128],"Results":[129],"suggest":[130],"that":[131],"demonstrates":[135],"robust":[136],"performance":[137],"state-of-the-art":[141],"methods":[142],"both":[144],"accuracy":[146],"and":[147],"total":[148],"turnaround.":[150]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":10},{"year":2021,"cited_by_count":6},{"year":2020,"cited_by_count":6},{"year":2019,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}