{"id":"https://openalex.org/W2920018717","doi":"https://doi.org/10.1109/tencon.2018.8650333","title":"Employing CNN to Identify Noisy Documents Thereafter Accomplishing Text Line Segmentation","display_name":"Employing CNN to Identify Noisy Documents Thereafter Accomplishing Text Line Segmentation","publication_year":2018,"publication_date":"2018-10-01","ids":{"openalex":"https://openalex.org/W2920018717","doi":"https://doi.org/10.1109/tencon.2018.8650333","mag":"2920018717"},"language":"en","primary_location":{"id":"doi:10.1109/tencon.2018.8650333","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tencon.2018.8650333","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"TENCON 2018 - 2018 IEEE Region 10 Conference","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/A5016931742","display_name":"Bhagesh Seraogi","orcid":"https://orcid.org/0000-0002-2831-6029"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Bhagesh Seraogi","raw_affiliation_strings":["ARC Document Solutions, Kolkata, India"],"affiliations":[{"raw_affiliation_string":"ARC Document Solutions, Kolkata, India","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027598019","display_name":"Supriya Das","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Supriya Das","raw_affiliation_strings":["ARC Document Solutions, Kolkata, India"],"affiliations":[{"raw_affiliation_string":"ARC Document Solutions, Kolkata, India","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103453088","display_name":"Purnendu Banerjee","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Purnendu Banerjee","raw_affiliation_strings":["ARC Document Solutions, Kolkata, India"],"affiliations":[{"raw_affiliation_string":"ARC Document Solutions, Kolkata, India","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049502948","display_name":"Himadri Majumder","orcid":"https://orcid.org/0000-0002-5059-5984"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Himadri Majumder","raw_affiliation_strings":["ARC Document Solutions, Kolkata, India"],"affiliations":[{"raw_affiliation_string":"ARC Document Solutions, Kolkata, India","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113707271","display_name":"Srinivas Mukkamala","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Srinivas Mukkamala","raw_affiliation_strings":["ARC Document Solutions, Kolkata, India"],"affiliations":[{"raw_affiliation_string":"ARC Document Solutions, Kolkata, India","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084390984","display_name":"Rahul Roy","orcid":"https://orcid.org/0000-0003-3329-8803"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rahul Roy","raw_affiliation_strings":["ARC Document Solutions, Kolkata, India"],"affiliations":[{"raw_affiliation_string":"ARC Document Solutions, Kolkata, India","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5025093894","display_name":"B.B. Chaudhuri","orcid":"https://orcid.org/0000-0003-0297-8929"},"institutions":[{"id":"https://openalex.org/I6498739","display_name":"Indian Statistical Institute","ror":"https://ror.org/00q2w1j53","country_code":"IN","type":"education","lineage":["https://openalex.org/I6498739"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Bidyut B. Chaudhuri","raw_affiliation_strings":["CVPR Unit, Indian Statistical Institute, Kolkata, India"],"affiliations":[{"raw_affiliation_string":"CVPR Unit, Indian Statistical Institute, Kolkata, India","institution_ids":["https://openalex.org/I6498739"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5016931742"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.1045,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.49220148,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1284","last_page":"1289"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10601","display_name":"Handwritten Text Recognition Techniques","score":1.0,"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":1.0,"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":0.9983999729156494,"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/T12357","display_name":"Digital Media Forensic Detection","score":0.9961000084877014,"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":0.8239201307296753},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6801311373710632},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.650791347026825},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6222495436668396},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.6008115410804749},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5852131843566895},{"id":"https://openalex.org/keywords/character","display_name":"Character (mathematics)","score":0.5653327107429504},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5443896055221558},{"id":"https://openalex.org/keywords/line","display_name":"Line (geometry)","score":0.5165256857872009},{"id":"https://openalex.org/keywords/projection","display_name":"Projection (relational algebra)","score":0.49826955795288086},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4629477262496948},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4406934976577759},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.18073207139968872},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.08121848106384277}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8239201307296753},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6801311373710632},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.650791347026825},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6222495436668396},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.6008115410804749},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5852131843566895},{"id":"https://openalex.org/C2780861071","wikidata":"https://www.wikidata.org/wiki/Q1062934","display_name":"Character (mathematics)","level":2,"score":0.5653327107429504},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5443896055221558},{"id":"https://openalex.org/C198352243","wikidata":"https://www.wikidata.org/wiki/Q37105","display_name":"Line (geometry)","level":2,"score":0.5165256857872009},{"id":"https://openalex.org/C57493831","wikidata":"https://www.wikidata.org/wiki/Q3134666","display_name":"Projection (relational algebra)","level":2,"score":0.49826955795288086},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4629477262496948},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4406934976577759},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.18073207139968872},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.08121848106384277},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","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/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tencon.2018.8650333","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tencon.2018.8650333","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"TENCON 2018 - 2018 IEEE Region 10 Conference","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1665214252","https://openalex.org/W1970809167","https://openalex.org/W1973960870","https://openalex.org/W2033801164","https://openalex.org/W2085998942","https://openalex.org/W2095705004","https://openalex.org/W2112796928","https://openalex.org/W2139684818","https://openalex.org/W2199243639","https://openalex.org/W2440214075","https://openalex.org/W2810662852","https://openalex.org/W2964121744","https://openalex.org/W6631190155","https://openalex.org/W6637242042","https://openalex.org/W6674330103"],"related_works":["https://openalex.org/W2393609567","https://openalex.org/W2369369044","https://openalex.org/W1503216044","https://openalex.org/W2354143083","https://openalex.org/W2372906645","https://openalex.org/W4319998713","https://openalex.org/W2366269494","https://openalex.org/W2353650902","https://openalex.org/W2347884623","https://openalex.org/W2361201432"],"abstract_inverted_index":{"Due":[0],"to":[1,16,42,79,91,104,124,170,178,205,228],"the":[2,31,34,43,48,81,88,108,126,136,154,172,180,207,210,214,221],"presence":[3],"of":[4,7,33,45,60,93,175,209],"high":[5,18],"volume":[6],"noise":[8,46,112],"in":[9,57,161],"text":[10,22,49,155,164,222],"documents,":[11],"it":[12,55,216],"becomes":[13,67],"very":[14],"difficult":[15],"achieve":[17],"accuracy":[19,225],"while":[20],"performing":[21],"line":[23,35,156,223],"segmentation.":[24],"Hence,":[25],"approaches":[26],"which":[27,151],"are":[28,86,203],"dependent":[29],"on":[30,190],"performance":[32],"segmentation":[36,224],"stage,":[37],"also":[38,199],"suffers.":[39],"Also":[40],"due":[41],"variability":[44],"patterns,":[47],"contents":[50],"get":[51],"distorted":[52],"and":[53,122,142,158,167,198],"sometimes":[54],"results":[56],"broken":[58,173],"strokes":[59,174],"a":[61,68,96,119,162,176,191],"character.":[62],"Thus,":[63],"character":[64,177,181],"level":[65],"recognition":[66,182],"challenging":[69],"task":[70],"for":[71],"noisy":[72,94,130,163,196],"documents.":[73],"Our":[74],"proposed":[75,137,185,211],"method":[76,186],"is":[77,102,226],"aimed":[78],"address":[80],"aforesaid":[82],"challenges.":[83],"Since,":[84],"we":[85,134],"considering":[87],"input":[89,109],"image":[90,110,166],"be":[92,218],"kind,":[95],"convolutional":[97],"neural":[98],"network":[99],"(CNN)":[100],"architecture":[101],"introduced":[103],"initially":[105,152],"identify":[106,125],"whether":[107],"contains":[111],"or":[113,131],"not.":[114],"We":[115],"consider":[116],"this":[117],"as":[118],"two-class":[120],"problem":[121],"try":[123,169],"respective":[127],"classes,":[128],"i.e.":[129],"clean.":[132],"Then,":[133],"apply":[135],"piecewise":[138],"projection":[139],"profile":[140],"feature":[141],"an":[143],"adaptive":[144],"region":[145],"growing":[146],"based":[147],"two-stage":[148],"algorithmic":[149],"approach":[150],"identifies":[153],"upper":[157],"lower":[159],"boundaries":[160],"document":[165],"then":[168],"regroup":[171],"enhance":[179],"accuracy.":[183],"The":[184],"has":[187],"been":[188],"tested":[189],"large":[192],"size":[193],"dataset":[194],"containing":[195],"documents":[197],"different":[200],"quality":[201],"measures":[202],"computed":[204],"establish":[206],"effectiveness":[208],"method.":[212],"From":[213],"measures,":[215],"can":[217],"noted":[219],"that":[220],"comparable":[227],"other":[229],"similar":[230],"state-of-the-art":[231],"results.":[232]},"counts_by_year":[{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}