{"id":"https://openalex.org/W3002859417","doi":"https://doi.org/10.1145/3373419.3373446","title":"KNN-LOF Algorithm Based on Skew Detection and Correction for Myanmar Handwritten Documents","display_name":"KNN-LOF Algorithm Based on Skew Detection and Correction for Myanmar Handwritten Documents","publication_year":2019,"publication_date":"2019-11-08","ids":{"openalex":"https://openalex.org/W3002859417","doi":"https://doi.org/10.1145/3373419.3373446","mag":"3002859417"},"language":"en","primary_location":{"id":"doi:10.1145/3373419.3373446","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3373419.3373446","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2019 3rd International Conference on Advances in Image Processing","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/A5071348255","display_name":"Chit San Lwin","orcid":null},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]},{"id":"https://openalex.org/I3029052455","display_name":"Technological University Kyaing Tong","ror":"https://ror.org/040wknf84","country_code":"MM","type":"education","lineage":["https://openalex.org/I3029052455"]}],"countries":["CN","MM"],"is_corresponding":true,"raw_author_name":"Chit San Lwin","raw_affiliation_strings":["School of Computer Science and Technology, Harbin Institute of Technology, Harbin, P. R. China and Department of Mathematics, Kyaing Tong University, Kyaing Tong City, Shan State, Myanmar"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Harbin Institute of Technology, Harbin, P. R. China and Department of Mathematics, Kyaing Tong University, Kyaing Tong City, Shan State, Myanmar","institution_ids":["https://openalex.org/I3029052455","https://openalex.org/I204983213"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5011252957","display_name":"WU Xiang-qian","orcid":null},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wu Xiangqian","raw_affiliation_strings":["School of Computer Science and Technology, Harbin Institute of Technology, Harbin, P. R. China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Harbin Institute of Technology, Harbin, P. R. China","institution_ids":["https://openalex.org/I204983213"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5071348255"],"corresponding_institution_ids":["https://openalex.org/I204983213","https://openalex.org/I3029052455"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.17046834,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"5","last_page":"9"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10601","display_name":"Handwritten Text Recognition Techniques","score":0.9998999834060669,"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":0.9998999834060669,"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/T12549","display_name":"Image and Object Detection Techniques","score":0.9993000030517578,"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":0.9962999820709229,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/skew","display_name":"Skew","score":0.9040168523788452},{"id":"https://openalex.org/keywords/skewness","display_name":"Skewness","score":0.8953555822372437},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7560176849365234},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.6657964587211609},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6115745306015015},{"id":"https://openalex.org/keywords/character","display_name":"Character (mathematics)","score":0.5771278738975525},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5344065427780151},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.5304262638092041},{"id":"https://openalex.org/keywords/optical-character-recognition","display_name":"Optical character recognition","score":0.5277976393699646},{"id":"https://openalex.org/keywords/k-nearest-neighbors-algorithm","display_name":"k-nearest neighbors algorithm","score":0.48325544595718384},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3829895853996277},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.37029147148132324},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.11311635375022888},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.06087186932563782}],"concepts":[{"id":"https://openalex.org/C43711488","wikidata":"https://www.wikidata.org/wiki/Q7534783","display_name":"Skew","level":2,"score":0.9040168523788452},{"id":"https://openalex.org/C122342681","wikidata":"https://www.wikidata.org/wiki/Q330828","display_name":"Skewness","level":2,"score":0.8953555822372437},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7560176849365234},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.6657964587211609},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6115745306015015},{"id":"https://openalex.org/C2780861071","wikidata":"https://www.wikidata.org/wiki/Q1062934","display_name":"Character (mathematics)","level":2,"score":0.5771278738975525},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5344065427780151},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.5304262638092041},{"id":"https://openalex.org/C546480517","wikidata":"https://www.wikidata.org/wiki/Q167555","display_name":"Optical character recognition","level":3,"score":0.5277976393699646},{"id":"https://openalex.org/C113238511","wikidata":"https://www.wikidata.org/wiki/Q1071612","display_name":"k-nearest neighbors algorithm","level":2,"score":0.48325544595718384},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3829895853996277},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.37029147148132324},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11311635375022888},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.06087186932563782},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3373419.3373446","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3373419.3373446","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2019 3rd International Conference on Advances in Image Processing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W839570005","https://openalex.org/W1581904241","https://openalex.org/W1978002970","https://openalex.org/W1979900189","https://openalex.org/W2029263116","https://openalex.org/W2059425116","https://openalex.org/W2109574165","https://openalex.org/W2184297159","https://openalex.org/W2274232996","https://openalex.org/W2321748699","https://openalex.org/W2329641898","https://openalex.org/W2527009802","https://openalex.org/W2540650228","https://openalex.org/W2727857707","https://openalex.org/W2774986299","https://openalex.org/W2788271762","https://openalex.org/W2963804401"],"related_works":["https://openalex.org/W4289406402","https://openalex.org/W2896097814","https://openalex.org/W3087516072","https://openalex.org/W2502284897","https://openalex.org/W2913537149","https://openalex.org/W3082395339","https://openalex.org/W4315866209","https://openalex.org/W2559352488","https://openalex.org/W4243596578","https://openalex.org/W1622180344"],"abstract_inverted_index":{"Skew":[0],"detection":[1,26,30,46,147],"and":[2,47,92,107,126,144,148],"correction":[3,48,149],"play":[4],"key":[5],"roles":[6],"in":[7,36,68,75,80,142,150,162],"helping":[8],"character":[9,99],"recognition":[10,16],"processes":[11],"to":[12,61,103,133],"achieve":[13,159],"more":[14],"accurate":[15],"results.":[17],"In":[18,101],"this":[19,40,136],"paper,":[20],"we":[21,42,90,113,158],"bring":[22],"the":[23,52,69,76,105,110,116],"idea":[24],"of":[25,27,32,78,83,97,109,119],"outlier":[28,55],"into":[29],"skewness":[31,64,88,96,146],"Myanmar":[33,151],"handwritten":[34,71,121,152],"characters":[35],"printed":[37],"documents.":[38,153],"For":[39],"purpose,":[41],"present":[43],"a":[44],"novel":[45],"algorithm":[49,138],"based":[50],"on":[51,86],"k-nearest":[53],"neighbor-local":[54],"factors":[56],"algorithm.":[57],"The":[58,154],"aim":[59],"is":[60,74],"detect":[62,91],"local":[63,87,143],"that":[65,157],"quite":[66],"happens":[67],"human's":[70],"process":[72],"which":[73],"form":[77],"fluctuation":[79],"written":[81],"lines":[82],"text.":[84],"Based":[85],"detection,":[89],"further":[93],"correct":[94],"global":[95,145],"input":[98],"image.":[100],"order":[102],"reveal":[104],"efficiency":[106],"performance":[108],"proposed":[111,137],"algorithm,":[112],"first":[114],"prepare":[115],"datasets":[117],"composed":[118],"different":[120,163],"styles":[122],"with":[123],"various":[124],"fonts":[125],"skew":[127],"angles.":[128],"We":[129],"afterward":[130],"perform":[131,140],"experiments":[132],"witness":[134],"how":[135],"can":[139],"well":[141],"results":[155,161],"show":[156],"better":[160],"experimental":[164],"settings.":[165]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}