{"id":"https://openalex.org/W4416965882","doi":"https://doi.org/10.1109/access.2025.3639638","title":"A Two-Level Multi-Branch Convolutional Neural Network Framework for Handwritten Gujarati Character Recognition","display_name":"A Two-Level Multi-Branch Convolutional Neural Network Framework for Handwritten Gujarati Character Recognition","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4416965882","doi":"https://doi.org/10.1109/access.2025.3639638"},"language":null,"primary_location":{"id":"doi:10.1109/access.2025.3639638","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3639638","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2025.3639638","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5030153263","display_name":"Krishn Limbachiya","orcid":null},"institutions":[{"id":"https://openalex.org/I165831266","display_name":"Nirma University","ror":"https://ror.org/05qkq7x38","country_code":"IN","type":"education","lineage":["https://openalex.org/I165831266"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Krishn Limbachiya","raw_affiliation_strings":["Institute of Technology, Nirma University, Ahmedabad, Gujarat, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Technology, Nirma University, Ahmedabad, Gujarat, India","institution_ids":["https://openalex.org/I165831266"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5045959137","display_name":"Ankit Sharma","orcid":"https://orcid.org/0000-0002-4590-7277"},"institutions":[{"id":"https://openalex.org/I165831266","display_name":"Nirma University","ror":"https://ror.org/05qkq7x38","country_code":"IN","type":"education","lineage":["https://openalex.org/I165831266"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Ankit Sharma","raw_affiliation_strings":["Institute of Technology, Nirma University, Ahmedabad, Gujarat, India"],"raw_orcid":"https://orcid.org/0000-0002-4590-7277","affiliations":[{"raw_affiliation_string":"Institute of Technology, Nirma University, Ahmedabad, Gujarat, India","institution_ids":["https://openalex.org/I165831266"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.34070827,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"13","issue":null,"first_page":"205733","last_page":"205752"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10601","display_name":"Handwritten Text Recognition Techniques","score":0.9786999821662903,"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":0.9786999821662903,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.004100000020116568,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.0008999999845400453,"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/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7354000210762024},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7179999947547913},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5669000148773193},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.5432000160217285},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.5249999761581421},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.49470001459121704},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.45809999108314514},{"id":"https://openalex.org/keywords/categorical-variable","display_name":"Categorical variable","score":0.44519999623298645},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.43869999051094055}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8327000141143799},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7354000210762024},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7342000007629395},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7179999947547913},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5669000148773193},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.5432000160217285},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.5249999761581421},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.49470001459121704},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.45809999108314514},{"id":"https://openalex.org/C5274069","wikidata":"https://www.wikidata.org/wiki/Q2285707","display_name":"Categorical variable","level":2,"score":0.44519999623298645},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.43869999051094055},{"id":"https://openalex.org/C121144440","wikidata":"https://www.wikidata.org/wiki/Q669754","display_name":"Neocognitron","level":4,"score":0.396699994802475},{"id":"https://openalex.org/C193415008","wikidata":"https://www.wikidata.org/wiki/Q639681","display_name":"Network architecture","level":2,"score":0.3767000138759613},{"id":"https://openalex.org/C94124525","wikidata":"https://www.wikidata.org/wiki/Q912550","display_name":"Categorization","level":2,"score":0.3443000018596649},{"id":"https://openalex.org/C546480517","wikidata":"https://www.wikidata.org/wiki/Q167555","display_name":"Optical character recognition","level":3,"score":0.32659998536109924},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.3239000141620636},{"id":"https://openalex.org/C167981619","wikidata":"https://www.wikidata.org/wiki/Q1685498","display_name":"Cross entropy","level":3,"score":0.30489999055862427},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.30379998683929443},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.3034000098705292},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.30250000953674316},{"id":"https://openalex.org/C175202392","wikidata":"https://www.wikidata.org/wiki/Q2434543","display_name":"Time delay neural network","level":3,"score":0.27399998903274536},{"id":"https://openalex.org/C191178318","wikidata":"https://www.wikidata.org/wiki/Q2256906","display_name":"Thresholding","level":3,"score":0.2720000147819519},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.27059999108314514},{"id":"https://openalex.org/C203519979","wikidata":"https://www.wikidata.org/wiki/Q865360","display_name":"Jaccard index","level":3,"score":0.2662999927997589},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.26260000467300415},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.25940001010894775},{"id":"https://openalex.org/C2781140086","wikidata":"https://www.wikidata.org/wiki/Q557945","display_name":"Confusion","level":2,"score":0.25119999051094055}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/access.2025.3639638","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3639638","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1109/access.2025.3639638","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3639638","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W1974190708","https://openalex.org/W1990309971","https://openalex.org/W2044943596","https://openalex.org/W2045504397","https://openalex.org/W2115296920","https://openalex.org/W2157142340","https://openalex.org/W2337656762","https://openalex.org/W2772817552","https://openalex.org/W3034078835","https://openalex.org/W3119652687","https://openalex.org/W3194679237","https://openalex.org/W3211196679","https://openalex.org/W3216086350","https://openalex.org/W4214563584","https://openalex.org/W4280640810","https://openalex.org/W4288071784","https://openalex.org/W4367593842","https://openalex.org/W4388414185","https://openalex.org/W4399242833","https://openalex.org/W4408147554","https://openalex.org/W4412567119","https://openalex.org/W4413344073","https://openalex.org/W4413468805","https://openalex.org/W7083438306","https://openalex.org/W7089499701"],"related_works":[],"abstract_inverted_index":{"The":[0,111],"recognition":[1],"of":[2,15,27,90,182],"handwritten":[3],"Gujarati":[4],"characters":[5],"is":[6,38],"a":[7,24,33,41],"challenging":[8],"task":[9],"due":[10],"to":[11,72,157],"the":[12,16,147,173,178,192],"complex":[13],"structure":[14],"script,":[17],"which":[18],"includes":[19],"consonants,":[20],"vowels,":[21],"numerals,":[22],"and":[23,60,67,102,107,121,140,169,186],"wide":[25],"variety":[26],"conjuncts.":[28],"To":[29],"address":[30],"these":[31],"challenges,":[32],"new":[34],"hierarchical":[35],"classification":[36,82,180],"approach":[37],"proposed,":[39],"utilizing":[40],"two-level":[42],"multi-branch":[43],"convolutional":[44],"neural":[45],"network":[46],"framework.":[47],"This":[48],"architecture":[49,194],"integrates":[50],"pre-trained":[51],"networks\u2014Mobile":[52],"Neural":[53,61],"Network(MobileNet),":[54],"Visual":[55],"Geometry":[56],"Group":[57],"16-layer":[58],"model(VGG16),":[59],"Architecture":[62],"Search":[63],"Network(NASNet)\u2014as":[64],"base":[65],"models":[66],"incorporates":[68],"parallel":[69],"branches":[70],"corresponding":[71],"general":[73,118],"categories":[74],"(Consonants,":[75],"Vowels,":[76],"Numerals,":[77],"Conjuncts),":[78],"followed":[79],"by":[80,131,198],"fine-grained":[81],"within":[83,201],"each":[84],"branch.":[85],"A":[86],"self-generated":[87],"dataset,":[88],"consisting":[89],"186":[91],"classes":[92,208],"with":[93,136,209],"600":[94],"samples":[95],"each,":[96],"was":[97,129,155,161],"collected":[98],"through":[99],"structured":[100],"forms":[101],"processed":[103],"using":[104,141,164],"contour-based":[105],"segmentation":[106],"image":[108],"preprocessing":[109],"techniques.":[110],"BCNN":[112,193],"leverages":[113],"shared":[114],"lower":[115],"layers":[116,124],"for":[117,125],"feature":[119],"extraction":[120],"specialized":[122],"upper":[123],"category-specific":[126],"classification.":[127],"Fine-tuning":[128],"performed":[130],"modifying":[132],"activation":[133],"functions,":[134],"optimizing":[135],"Stochastic":[137],"Gradient":[138],"Descent,":[139],"Sparse":[142],"Categorical":[143],"Cross":[144],"Entropy":[145],"as":[146],"loss":[148],"function.":[149],"Early":[150],"stopping":[151],"over":[152],"10":[153],"epochs":[154],"employed":[156],"prevent":[158],"overfitting.":[159],"Evaluation":[160],"carried":[162],"out":[163],"accuracy,":[165],"precision,":[166],"recall,":[167],"F-score,":[168],"confusion":[170],"matrices.":[171],"Among":[172],"tested":[174],"models,":[175],"MobileNet":[176],"achieved":[177],"highest":[179],"accuracy":[181],"98.32%,":[183],"outperforming":[184],"VGG16":[185],"NasNet.":[187],"Comparative":[188],"results":[189],"showed":[190],"that":[191],"effectively":[195],"reduced":[196],"misclassification":[197],"confining":[199],"errors":[200],"respective":[202],"branches,":[203],"particularly":[204],"improving":[205],"performance":[206],"in":[207],"high":[210],"visual":[211],"similarity.":[212]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-12-03T00:00:00"}
