{"id":"https://openalex.org/W4417405271","doi":"https://doi.org/10.1109/asiancon66527.2025.11281074","title":"Synergistic Hybrid Segmentation for Handwritten Kannada Word Recognition Addressing Deep Learning Challenges","display_name":"Synergistic Hybrid Segmentation for Handwritten Kannada Word Recognition Addressing Deep Learning Challenges","publication_year":2025,"publication_date":"2025-08-22","ids":{"openalex":"https://openalex.org/W4417405271","doi":"https://doi.org/10.1109/asiancon66527.2025.11281074"},"language":"en","primary_location":{"id":"doi:10.1109/asiancon66527.2025.11281074","doi":"https://doi.org/10.1109/asiancon66527.2025.11281074","is_oa":false,"landing_page_url":"https://doi.org/10.1109/asiancon66527.2025.11281074","pdf_url":null,"source":{"id":"https://openalex.org/S4363607899","display_name":"Asian Conference on Innovation in Technology (ASIANCON)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"2025 5th Asian Conference on Innovation in Technology (ASIANCON)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"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/A5048928140","display_name":"M. Malini","orcid":"https://orcid.org/0009-0006-0055-2018","observed_orcids":["https://orcid.org/0009-0006-0055-2018"]},"institutions":[{"id":"https://openalex.org/I83737708","display_name":"REVA University","ror":"https://ror.org/03gtcxd54","country_code":"IN","type":"education","lineage":["https://openalex.org/I83737708"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Malini. M","raw_affiliation_strings":["REVA University, Rukmini Knowledge Park,Computer Science and Applications,Bangalore,Karnataka,India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"REVA University, Rukmini Knowledge Park,Computer Science and Applications,Bangalore,Karnataka,India","institution_ids":["https://openalex.org/I83737708"]}]},{"author_position":"last","author":{"id":null,"display_name":"Hemanth K S","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I48018076","display_name":"Christ University","ror":"https://ror.org/022tv9y30","country_code":"IN","type":"education","lineage":["https://openalex.org/I48018076"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Hemanth K S","raw_affiliation_strings":["CHRIST University,Department of Computer Science,Bangalore,Karnataka,India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"CHRIST University,Department of Computer Science,Bangalore,Karnataka,India","institution_ids":["https://openalex.org/I48018076"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"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":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"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":0.0,"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/T11398","display_name":"Hand Gesture Recognition Systems","score":0.0,"subfield":{"id":"https://openalex.org/subfields/1709","display_name":"Human-Computer Interaction"},"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/handwritten-text-segmentation","display_name":"handwritten text segmentation","score":0.7609999775886536},{"id":"https://openalex.org/keywords/hybrid-segmentation","display_name":"hybrid segmentation","score":0.6299999952316284},{"id":"https://openalex.org/keywords/feature-engineering","display_name":"feature engineering","score":0.5619999766349792},{"id":"https://openalex.org/keywords/low-resource-languages","display_name":"low-resource languages","score":0.7149999737739563}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7839999794960022},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7376999855041504},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.621999979019165},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5778999924659729},{"id":"https://openalex.org/C2780861071","wikidata":"https://www.wikidata.org/wiki/Q1062934","display_name":"Character (mathematics)","level":2,"score":0.5131999850273132},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5026000142097473},{"id":"https://openalex.org/C44868376","wikidata":"https://www.wikidata.org/wiki/Q3099089","display_name":"Intelligent character recognition","level":4,"score":0.47510001063346863},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4726000130176544},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.43459999561309814},{"id":"https://openalex.org/C2778579358","wikidata":"https://www.wikidata.org/wiki/Q33673","display_name":"Kannada","level":2,"score":0.4092000126838684},{"id":"https://openalex.org/C546480517","wikidata":"https://www.wikidata.org/wiki/Q167555","display_name":"Optical character recognition","level":3,"score":0.3930000066757202},{"id":"https://openalex.org/C112640561","wikidata":"https://www.wikidata.org/wiki/Q2440634","display_name":"Handwriting recognition","level":3,"score":0.3452000021934509},{"id":"https://openalex.org/C62354387","wikidata":"https://www.wikidata.org/wiki/Q875399","display_name":"Boundary (topology)","level":2,"score":0.3398999869823456},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.3366999924182892},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.3303000032901764},{"id":"https://openalex.org/C17649283","wikidata":"https://www.wikidata.org/wiki/Q6044162","display_name":"Intelligent word recognition","level":5,"score":0.32429999113082886},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3124000132083893},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3098999857902527},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.305400013923645},{"id":"https://openalex.org/C32717103","wikidata":"https://www.wikidata.org/wiki/Q184759","display_name":"Character encoding","level":3,"score":0.3052999973297119},{"id":"https://openalex.org/C2777462759","wikidata":"https://www.wikidata.org/wiki/Q18395344","display_name":"Word embedding","level":3,"score":0.3050000071525574},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.30059999227523804},{"id":"https://openalex.org/C2777601683","wikidata":"https://www.wikidata.org/wiki/Q6499736","display_name":"Vocabulary","level":2,"score":0.29490000009536743},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.2842999994754791},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.27639999985694885},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.2736999988555908},{"id":"https://openalex.org/C2779386606","wikidata":"https://www.wikidata.org/wiki/Q2393642","display_name":"Handwriting","level":2,"score":0.2711000144481659},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2703000009059906},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.25540000200271606},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.25519999861717224},{"id":"https://openalex.org/C2987247673","wikidata":"https://www.wikidata.org/wiki/Q167555","display_name":"Character recognition","level":3,"score":0.25200000405311584}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/asiancon66527.2025.11281074","doi":"https://doi.org/10.1109/asiancon66527.2025.11281074","is_oa":false,"landing_page_url":"https://doi.org/10.1109/asiancon66527.2025.11281074","pdf_url":null,"source":{"id":"https://openalex.org/S4363607899","display_name":"Asian Conference on Innovation in Technology (ASIANCON)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"2025 5th Asian Conference on Innovation in Technology (ASIANCON)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":8,"referenced_works":["https://openalex.org/W2939778902","https://openalex.org/W2955012375","https://openalex.org/W4225303552","https://openalex.org/W4402753871","https://openalex.org/W4406679900","https://openalex.org/W4408862613","https://openalex.org/W4411574657","https://openalex.org/W4412690847"],"related_works":["https://openalex.org/W7202026175","https://openalex.org/W7202242207","https://openalex.org/W2091558740","https://openalex.org/W3008353764","https://openalex.org/W7160878164","https://openalex.org/W2183185333","https://openalex.org/W4230348851","https://openalex.org/W7217677839","https://openalex.org/W3003516670","https://openalex.org/W7131388510"],"abstract_inverted_index":{"The":[0,27],"handwritten":[1,93,209],"Kannada":[2,94,201],"script":[3],"has":[4],"an":[5],"intricate":[6],"aksharas":[7],"that":[8,68,78,171],"are":[9],"formed":[10],"by":[11,175],"combining":[12],"consonants,":[13],"vowels,":[14],"and":[15,52,70,138,146,180,190],"ottus.":[16],"These":[17],"complex":[18,200],"combinations":[19],"pose":[20],"significant":[21,124,139],"hurdles":[22],"for":[23,129,135,142,199,207],"automated":[24,208],"text":[25,210],"segmentation.":[26],"inherent":[28],"diversity":[29],"in":[30,92,212],"handwriting":[31],"styles,":[32],"coupled":[33],"with":[34,153],"prevalent":[35],"character":[36,61,90,99],"overlap,":[37,100],"multi-touch":[38,103],"connections,":[39],"varied":[40],"curve":[41,50],"structures":[42],"such":[43,213],"as":[44],"upper":[45,48],"open":[46],"curve(OC),":[47],"closed":[49],"(CC),":[51],"the":[53],"highly":[54],"condensed":[55],"nature":[56],"of":[57],"Ottaksharas,":[58],"routinely":[59],"blurs":[60],"boundaries,":[62],"leading":[63],"to":[64,87,116,164],"severe":[65],"segmentation":[66,198],"errors":[67],"propagate":[69],"compromise":[71],"overall":[72],"recognition":[73],"accuracy.":[74],"A":[75],"hybrid":[76],"approach":[77],"customizes":[79],"adaptive":[80],"traditional":[81,173],"methods":[82],"like":[83,156],"vertical":[84],"pixel":[85],"count,":[86],"identify":[88],"true":[89],"gaps":[91],"characters":[95,104],"could":[96],"effectively":[97],"manage":[98],"or":[101,105],"segment":[102],"Ottaksharas.":[106],"This":[107,121,168,193],"pre-processing":[108],"stage":[109],"can":[110,185],"allow":[111],"subsequent":[112],"deep":[113,159],"learning":[114,160],"models":[115,161],"recognize":[117],"this":[118],"segmented":[119],"character.":[120],"will":[122,162],"avoid":[123],"hurdles:":[125],"immense":[126],"data":[127],"requirements":[128],"pixel-level":[130],"annotations,":[131],"high":[132],"computational":[133],"costs":[134],"dense":[136],"prediction,":[137],"architectural":[140],"complexities":[141],"precise":[143],"boundary":[144],"delineation":[145],"handling":[147],"connectivity.":[148],"Given":[149],"these":[150],"constraints,":[151],"particularly":[152],"less-resource":[154],"language":[155],"Kannada,":[157],"scaling":[158],"lead":[163],"ever":[165],"erroneous":[166],"recognition.":[167],"paper":[169],"argues":[170],"modified":[172],"approaches,":[174],"directly":[176],"embedding":[177],"customized":[178],"knowledge":[179],"leveraging":[181],"targeted":[182],"feature":[183],"engineering,":[184],"offer":[186],"a":[187,204],"computationally":[188],"efficient":[189],"data-lean":[191],"alternative.":[192],"strategy":[194],"enables":[195],"more":[196],"robust":[197],"characters,":[202],"providing":[203],"practical":[205],"pathway":[206],"processing":[211],"linguistic":[214],"domains.":[215]},"counts_by_year":[],"updated_date":"2026-10-28T10:33:24.094632","created_date":"2025-12-16T00:00:00"}