{"id":"https://openalex.org/W7204484599","doi":"https://doi.org/10.20944/preprints202608.2007.v1","title":"CharNetwork Acter-Level Bengali OCR Using a Custom Convolutional Neural","display_name":"CharNetwork Acter-Level Bengali OCR Using a Custom Convolutional Neural","publication_year":2026,"publication_date":"2026-08-27","ids":{"openalex":"https://openalex.org/W7204484599","doi":"https://doi.org/10.20944/preprints202608.2007.v1"},"language":"en","primary_location":{"id":"doi:10.20944/preprints202608.2007.v1","doi":"https://doi.org/10.20944/preprints202608.2007.v1","is_oa":true,"landing_page_url":"https://doi.org/10.20944/preprints202608.2007.v1","pdf_url":null,"source":{"id":"https://openalex.org/S6309402219","display_name":"Preprints.org","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"posted-content"},"parent_works":[],"lineage":["https://openalex.org/W7204484599"],"child_works_count":0,"type":"preprint","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.20944/preprints202608.2007.v1","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5139564601","display_name":"Akter Khadiza","orcid":"https://orcid.org/0009-0001-8186-1499","observed_orcids":["https://orcid.org/0009-0001-8186-1499","https://orcid.org/0009-0004-8003-9155","https://orcid.org/0009-0004-8004-2688"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Khadiza Akter","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0009-0001-8186-1499","affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5077259822","display_name":"Kazi Abdul Mannan","orcid":"https://orcid.org/0000-0002-7123-132X","observed_orcids":["https://orcid.org/0000-0002-7123-132X"]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Kazi Abdul Mannan","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0002-7123-132X","affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5077259822"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10601","display_name":"Handwritten Text Recognition Techniques","score":0.9994999766349792,"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":0.9994999766349792,"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":0.0005000000237487257,"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":9.999999747378752e-05,"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/isolated-character-recognition","display_name":"isolated character recognition","score":0.9359999895095825},{"id":"https://openalex.org/keywords/lightweight-convolutional-neural-network","display_name":"lightweight convolutional neural network","score":0.9779999852180481},{"id":"https://openalex.org/keywords/resource-limited-environments","display_name":"resource-constrained environments","score":0.6850000023841858},{"id":"https://openalex.org/keywords/document-digitization","display_name":"document digitization","score":0.746999979019165},{"id":"https://openalex.org/keywords/diacritics","display_name":"diacritics","score":0.6050000190734863}],"concepts":[{"id":"https://openalex.org/C19235068","wikidata":"https://www.wikidata.org/wiki/Q9610","display_name":"Bengali","level":2,"score":0.9340000152587891},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8288999795913696},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7700999975204468},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7084000110626221},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.6132000088691711},{"id":"https://openalex.org/C136886441","wikidata":"https://www.wikidata.org/wiki/Q926129","display_name":"Normalization (sociology)","level":2,"score":0.5924000144004822},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5364000201225281},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5342000126838684},{"id":"https://openalex.org/C546480517","wikidata":"https://www.wikidata.org/wiki/Q167555","display_name":"Optical character recognition","level":3,"score":0.4787999987602234},{"id":"https://openalex.org/C78201319","wikidata":"https://www.wikidata.org/wiki/Q685727","display_name":"Grayscale","level":3,"score":0.4553999900817871},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.43810001015663147},{"id":"https://openalex.org/C2987247673","wikidata":"https://www.wikidata.org/wiki/Q167555","display_name":"Character recognition","level":3,"score":0.420199990272522},{"id":"https://openalex.org/C16910744","wikidata":"https://www.wikidata.org/wiki/Q7705759","display_name":"Test data","level":2,"score":0.362199991941452},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35420000553131104},{"id":"https://openalex.org/C2780861071","wikidata":"https://www.wikidata.org/wiki/Q1062934","display_name":"Character (mathematics)","level":2,"score":0.35190001130104065},{"id":"https://openalex.org/C2779308522","wikidata":"https://www.wikidata.org/wiki/Q843958","display_name":"Digitization","level":2,"score":0.3370000123977661},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.336899995803833},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3278000056743622},{"id":"https://openalex.org/C2776145597","wikidata":"https://www.wikidata.org/wiki/Q25339462","display_name":"Dropout (neural networks)","level":2,"score":0.3091000020503998},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.3086000084877014},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.30379998683929443},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2838999927043915},{"id":"https://openalex.org/C53533937","wikidata":"https://www.wikidata.org/wiki/Q185020","display_name":"Histogram","level":3,"score":0.26820001006126404},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.2653000056743622},{"id":"https://openalex.org/C10551718","wikidata":"https://www.wikidata.org/wiki/Q5227332","display_name":"Data pre-processing","level":2,"score":0.25859999656677246},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2554999887943268}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.20944/preprints202608.2007.v1","doi":"https://doi.org/10.20944/preprints202608.2007.v1","is_oa":true,"landing_page_url":"https://doi.org/10.20944/preprints202608.2007.v1","pdf_url":null,"source":{"id":"https://openalex.org/S6309402219","display_name":"Preprints.org","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"posted-content"}],"best_oa_location":{"id":"doi:10.20944/preprints202608.2007.v1","doi":"https://doi.org/10.20944/preprints202608.2007.v1","is_oa":true,"landing_page_url":"https://doi.org/10.20944/preprints202608.2007.v1","pdf_url":null,"source":{"id":"https://openalex.org/S6309402219","display_name":"Preprints.org","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"posted-content"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W7202248117","https://openalex.org/W7155201128","https://openalex.org/W4210623551","https://openalex.org/W3183766727","https://openalex.org/W7160413422","https://openalex.org/W7164744425","https://openalex.org/W4411097511","https://openalex.org/W3162332277","https://openalex.org/W4282964639","https://openalex.org/W4407254884"],"abstract_inverted_index":{"Optical":[0],"Character":[1,74],"Recognition":[2,75],"(OCR)":[3],"for":[4,50,66,106,187],"the":[5,15,72,116,153,182],"Bengali":[6,68,73,189],"language":[7],"remains":[8],"a":[9,58,97,138],"challenging":[10],"research":[11],"problem":[12],"because":[13],"of":[14,130],"script's":[16],"complex":[17],"morphology,":[18],"visually":[19],"similar":[20],"characters,":[21],"compound":[22],"strokes,":[23],"and":[24,92,104,109,127,133,155,178,196],"diacritical":[25],"marks.":[26],"Although":[27],"recent":[28],"deep":[29],"learning":[30,45],"approaches":[31],"have":[32],"significantly":[33],"improved":[34,110],"recognition":[35,70,176],"performance,":[36],"many":[37,144],"existing":[38],"models":[39],"rely":[40],"on":[41],"computationally":[42],"intensive":[43],"transfer":[44],"architectures":[46,169],"that":[47,115,166],"are":[48],"unsuitable":[49],"real-time":[51],"or":[52],"resource-constrained":[53],"environments.":[54],"This":[55],"study":[56],"proposes":[57],"lightweight":[59,167],"Convolutional":[60],"Neural":[61],"Network":[62],"(CNN)":[63],"specifically":[64],"designed":[65],"isolated":[67],"character":[69],"using":[71],"Dataset":[76],"(BCRD).":[77],"The":[78,148],"proposed":[79,117,183],"framework":[80],"incorporates":[81],"image":[82],"preprocessing":[83],"techniques,":[84],"including":[85],"grayscale":[86],"conversion,":[87],"Otsu":[88],"thresholding,":[89],"normalization,":[90],"resizing,":[91],"data":[93],"augmentation,":[94],"followed":[95],"by":[96],"compact":[98],"CNN":[99,168],"architecture":[100],"employing":[101],"Batch":[102],"Normalization":[103],"Dropout":[105],"stable":[107],"training":[108,154],"generalization.":[111],"Experimental":[112],"evaluation":[113],"demonstrates":[114],"model":[118],"achieves":[119],"97.1%":[120],"test":[121],"accuracy,":[122],"with":[123],"corresponding":[124],"Precision,":[125],"Recall,":[126],"F1-score":[128],"values":[129],"97.0%,":[131],"96.8%,":[132],"96.9%,":[134],"respectively,":[135],"while":[136],"maintaining":[137],"substantially":[139],"lower":[140],"computational":[141,179],"complexity":[142],"than":[143],"large":[145],"pre-trained":[146],"networks.":[147],"small":[149],"performance":[150],"gap":[151],"between":[152,175],"testing":[156],"datasets":[157],"further":[158],"indicates":[159],"excellent":[160],"generalization":[161],"capability.":[162],"These":[163],"findings":[164],"demonstrate":[165],"can":[170],"provide":[171],"an":[172],"effective":[173],"balance":[174],"accuracy":[177],"efficiency,":[180],"making":[181],"approach":[184],"well":[185],"suited":[186],"practical":[188],"OCR":[190],"applications,":[191],"mobile":[192],"devices,":[193],"embedded":[194],"systems,":[195],"intelligent":[197],"document":[198],"digitization":[199],"platforms.":[200]},"counts_by_year":[],"updated_date":"2026-11-09T13:22:03.289127","created_date":"2026-08-28T00:00:00"}