{"id":"https://openalex.org/W3195390734","doi":"https://doi.org/10.3233/jifs-210015","title":"Document image binarization using difference of concatenated convolutions","display_name":"Document image binarization using difference of concatenated convolutions","publication_year":2021,"publication_date":"2021-08-10","ids":{"openalex":"https://openalex.org/W3195390734","doi":"https://doi.org/10.3233/jifs-210015","mag":"3195390734"},"language":"en","primary_location":{"id":"doi:10.3233/jifs-210015","is_oa":false,"landing_page_url":"https://doi.org/10.3233/jifs-210015","pdf_url":null,"source":{"id":"https://openalex.org/S179157397","display_name":"Journal of Intelligent & Fuzzy Systems","issn_l":"1064-1246","issn":["1064-1246","1875-8967"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Intelligent &amp; Fuzzy Systems","raw_type":"journal-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/A5009845980","display_name":"R Jyothi","orcid":"https://orcid.org/0000-0001-8767-526X"},"institutions":[{"id":"https://openalex.org/I158338959","display_name":"University of Kerala","ror":"https://ror.org/05tqa9940","country_code":"IN","type":"education","lineage":["https://openalex.org/I158338959"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"R.L. Jyothi","raw_affiliation_strings":["University of Kerala"],"affiliations":[{"raw_affiliation_string":"University of Kerala","institution_ids":["https://openalex.org/I158338959"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5110875245","display_name":"M. Abdul Rahiman","orcid":null},"institutions":[{"id":"https://openalex.org/I4390039265","display_name":"PRG S&Tech (South Korea)","ror":"https://ror.org/02sr2ee22","country_code":null,"type":"company","lineage":["https://openalex.org/I4390039265"]}],"countries":[],"is_corresponding":false,"raw_author_name":"M. Abdul Rahiman","raw_affiliation_strings":["LBS Center for Science and Technology"],"affiliations":[{"raw_affiliation_string":"LBS Center for Science and Technology","institution_ids":["https://openalex.org/I4390039265"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5009845980"],"corresponding_institution_ids":["https://openalex.org/I158338959"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.10099673,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":"41","issue":"2","first_page":"2939","last_page":"2952"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10601","display_name":"Handwritten Text Recognition Techniques","score":0.9991000294685364,"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":0.9991000294685364,"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/T12707","display_name":"Vehicle License Plate Recognition","score":0.992900013923645,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12549","display_name":"Image and Object Detection Techniques","score":0.9848999977111816,"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":0.8466739654541016},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.646030068397522},{"id":"https://openalex.org/keywords/concatenation","display_name":"Concatenation (mathematics)","score":0.6394135355949402},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5977484583854675},{"id":"https://openalex.org/keywords/thresholding","display_name":"Thresholding","score":0.5678933262825012},{"id":"https://openalex.org/keywords/layer","display_name":"Layer (electronics)","score":0.5593335032463074},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5499829053878784},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5496373176574707},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.5317942500114441},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5155563354492188},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4937504231929779},{"id":"https://openalex.org/keywords/dependency","display_name":"Dependency (UML)","score":0.47663888335227966},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.433349072933197},{"id":"https://openalex.org/keywords/historical-document","display_name":"Historical document","score":0.4254588484764099},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3537479639053345},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.32589173316955566},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.10252964496612549}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8466739654541016},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.646030068397522},{"id":"https://openalex.org/C87619178","wikidata":"https://www.wikidata.org/wiki/Q126002","display_name":"Concatenation (mathematics)","level":2,"score":0.6394135355949402},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5977484583854675},{"id":"https://openalex.org/C191178318","wikidata":"https://www.wikidata.org/wiki/Q2256906","display_name":"Thresholding","level":3,"score":0.5678933262825012},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.5593335032463074},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5499829053878784},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5496373176574707},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.5317942500114441},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5155563354492188},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4937504231929779},{"id":"https://openalex.org/C19768560","wikidata":"https://www.wikidata.org/wiki/Q320727","display_name":"Dependency (UML)","level":2,"score":0.47663888335227966},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.433349072933197},{"id":"https://openalex.org/C2778371909","wikidata":"https://www.wikidata.org/wiki/Q3771738","display_name":"Historical document","level":2,"score":0.4254588484764099},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3537479639053345},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.32589173316955566},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10252964496612549},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"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/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/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.3233/jifs-210015","is_oa":false,"landing_page_url":"https://doi.org/10.3233/jifs-210015","pdf_url":null,"source":{"id":"https://openalex.org/S179157397","display_name":"Journal of Intelligent & Fuzzy Systems","issn_l":"1064-1246","issn":["1064-1246","1875-8967"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Intelligent &amp; Fuzzy Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":8,"referenced_works":["https://openalex.org/W1984153831","https://openalex.org/W2008748102","https://openalex.org/W2027091505","https://openalex.org/W2083601800","https://openalex.org/W2083970667","https://openalex.org/W2115235609","https://openalex.org/W3022283484","https://openalex.org/W6653071074"],"related_works":["https://openalex.org/W2373577936","https://openalex.org/W4221148444","https://openalex.org/W899286121","https://openalex.org/W2100347543","https://openalex.org/W2187556787","https://openalex.org/W2099809257","https://openalex.org/W2043882028","https://openalex.org/W3020778960","https://openalex.org/W2102970464","https://openalex.org/W1951707349"],"abstract_inverted_index":{"Binarization":[0],"is":[1,83,98,108,164,213,238,261],"the":[2,78,102,150,161,168,202,210,245,255,258],"most":[3,243],"important":[4,233],"stage":[5],"in":[6,26,144,149,176,218,249],"historical":[7,39,86,204],"document":[8,64,87,134,226,250],"image":[9],"processing.":[10],"Efficient":[11],"working":[12,159],"of":[13,32,37,62,77,113,131,152,158,160,170,188,195,209,225,235,244,257],"character":[14],"and":[15,34,120,139,172,266],"word":[16],"recognition":[17],"algorithms":[18,25,68],"depend":[19,28],"on":[20,29],"effective":[21,60],"segmentation":[22],"methods.":[23],"Segmentation":[24],"turn":[27],"images":[30,94,97,227],"free":[31],"noises":[33],"degradations.":[35],"Most":[36],"these":[38,63],"documents":[40,198],"are":[41],"illegible":[42],"with":[43,181,192,201,263],"degradations":[44],"like":[45],"bleeding":[46],"through":[47],"degradation,":[48],"faded":[49],"ink":[50],"or":[51],"faint":[52],"characters,":[53],"uneven":[54],"illumination,":[55],"contrast":[56],"variation,":[57],"etc.":[58],"For":[59],"processing":[61],"images,":[65],"efficient":[66],"binarization":[67,130],"should":[69],"be":[70],"devised.":[71],"Here":[72],"a":[73,193,219],"simple":[74],"modified":[75,99,186],"version":[76,187],"Convolutional":[79],"Neural":[80],"Network":[81],"(CNN)":[82],"proposed":[84,103,162,211,259],"for":[85,91,129,222],"binarization.":[88],"AOD-Net":[89,124],"architecture":[90],"generating":[92],"dehazed":[93],"from":[95],"hazed":[96],"to":[100,125],"create":[101],"network.The":[104],"new":[105],"CNN":[106,189],"model":[107,163,260],"created":[109],"by":[110,166,173],"incorporating":[111],"Difference":[112],"Concatenation":[114],"layer":[115,118,122,138,141],"(DOC),":[116],"Enhancement":[117],"(EN)":[119],"Thresholding":[121],"into":[123],"make":[126],"it":[127,215,240],"suitable":[128],"highly":[132,196],"degraded":[133,197],"images.":[135,251],"The":[136,156,206],"DOC":[137],"EN":[140],"work":[142,180],"effectively":[143,191],"solving":[145],"degradation":[146,246],"that":[147,179,214,239],"exists":[148],"form":[151],"low":[153,182],"pass":[154],"noises.":[155],"complexity":[157],"reduced":[165],"decreasing":[167],"number":[169],"layers":[171,178],"introducing":[174],"filters":[175],"convolution":[177],"inter-pixel":[183],"dependency.":[184],"This":[185],"works":[190,216],"variety":[194],"when":[199],"tested":[200],"benchmark":[203],"datasets.":[205],"main":[207],"highlight":[208,234],"network":[212],"efficiently":[217],"generalized":[220],"manner":[221],"any":[223],"type":[224],"without":[228],"further":[229],"parameter":[230],"tuning.":[231],"Another":[232],"this":[236,253],"method":[237],"can":[241],"handle":[242],"categories":[247],"present":[248],"In":[252],"work,":[254],"performance":[256],"compared":[262],"Otsu,":[264],"Sauvola,":[265],"three":[267],"recent":[268],"Deep":[269],"Learning-based":[270],"models.":[271]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
