{"created":"2020-09-01T14:54:34.581361+00:00","id":4482,"links":{},"metadata":{"_buckets":{"deposit":"475803e8-1d80-4bb5-b589-b29597ff297a"},"_deposit":{"id":"4482","owners":[],"pid":{"revision_id":0,"type":"recid","value":"4482"},"status":"published"},"_oai":{"id":"oai:meral.edu.mm:recid/4482","sets":["1582963302567:1597824273898"]},"communities":["ucsy"],"item_1583103067471":{"attribute_name":"Title","attribute_value_mlt":[{"subitem_1551255647225":"Handwritten Character Recognition based on Competitive Neural Trees","subitem_1551255648112":"en_US"}]},"item_1583103085720":{"attribute_name":"Description","attribute_value_mlt":[{"interim":"Competitive Neural Trees (CNeT) are widely used forclassification in pattern recognition. This paperapplies this technique for recognizing of Myanmarhandwritten characters. This paper involves threeimportant steps, typically preprocessing, featureextraction and classification. The aim ofpreprocessing is to improve the quality of the imagesfor further processing. For the extraction of features,four of the 3×3 masks are applied to word images toextract horizontal, vertical, right and left-diagonallines. Afterwards, decomposed images should bepartitioned to eight sectors around the center ofimage and the number of black pixels in each sectorcalculated and normalized by dividing them upon thetotal number of black pixels in word images forfeature vector. These feature vector from word imageswhich are used in Competitive Neural Trees (CNeT)for recognition purpose. This paper introduces aglobal search method for the CNeT, which is utilizedfor training."}]},"item_1583103108160":{"attribute_name":"Keywords","attribute_value_mlt":[{"interim":"Myanmar handwritten characters"},{"interim":"CNeT"},{"interim":"global search method"}]},"item_1583103120197":{"attribute_name":"Files","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_access","date":[{"dateType":"Available","dateValue":"2019-11-14"}],"displaytype":"preview","filename":"10055.pdf","filesize":[{"value":"849 Kb"}],"format":"application/pdf","licensetype":"license_note","mimetype":"application/pdf","url":{"url":"https://meral.edu.mm/record/4482/files/10055.pdf"},"version_id":"93d3db90-2fef-4a34-af91-5a12620a326b"}]},"item_1583103131163":{"attribute_name":"Journal articles","attribute_value_mlt":[{"subitem_issue":"","subitem_journal_title":"Tenth International Conference On Computer Applications (ICCA 2012)","subitem_pages":"","subitem_volume":""}]},"item_1583103147082":{"attribute_name":"Conference papers","attribute_value_mlt":[{"subitem_acronym":"","subitem_c_date":"","subitem_conference_title":"","subitem_part":"","subitem_place":"","subitem_session":"","subitem_website":""}]},"item_1583103211336":{"attribute_name":"Books/reports/chapters","attribute_value_mlt":[{"subitem_book_title":"","subitem_isbn":"","subitem_pages":"","subitem_place":"","subitem_publisher":""}]},"item_1583103233624":{"attribute_name":"Thesis/dissertations","attribute_value_mlt":[{"subitem_awarding_university":"","subitem_supervisor(s)":[{"subitem_supervisor":""}]}]},"item_1583105942107":{"attribute_name":"Authors","attribute_value_mlt":[{"subitem_authors":[{"subitem_authors_fullname":"Htike, Theingi"},{"subitem_authors_fullname":"Thein, Yadana"}]}]},"item_1583108359239":{"attribute_name":"Upload type","attribute_value_mlt":[{"interim":"Publication"}]},"item_1583108428133":{"attribute_name":"Publication type","attribute_value_mlt":[{"interim":"Article"}]},"item_1583159729339":{"attribute_name":"Publication date","attribute_value":"2012-02-28"},"item_1583159847033":{"attribute_name":"Identifier","attribute_value":"http://onlineresource.ucsy.edu.mm/handle/123456789/2410"},"item_title":"Handwritten Character Recognition based on Competitive Neural Trees","item_type_id":"21","owner":"1","path":["1597824273898"],"publish_date":"2019-11-14","publish_status":"0","recid":"4482","relation_version_is_last":true,"title":["Handwritten Character Recognition based on Competitive Neural Trees"],"weko_creator_id":"1","weko_shared_id":-1},"updated":"2021-12-13T03:29:23.850245+00:00"}