{"_buckets": {"deposit": "e7e1b5c4-740e-49ef-a0f6-ac5a14354347"}, "_deposit": {"created_by": 71, "id": "7422", "owner": "71", "owners": [71], "owners_ext": {"displayname": "Kay_Thwe", "username": "kay_thwe"}, "pid": {"revision_id": 0, "type": "depid", "value": "7422"}, "status": "published"}, "_oai": {"id": "oai:meral.edu.mm:recid/00007422", "sets": ["user-miit"]}, "communities": ["miit"], "item_1583103067471": {"attribute_name": "Title", "attribute_value_mlt": [{"subitem_1551255647225": "Myanmar Handwritten Digit and Character Recognition Using Blocked Features and Random Forest Classifier", "subitem_1551255648112": "en"}]}, "item_1583103085720": {"attribute_name": "Description", "attribute_value_mlt": [{"interim": "Automatic recognition of handwritten digits and handwritten characters has been studied in the\npattern recognition field for many years. The handwritten digits and character recognition are still a\nsignificant field of study, due to its wide practical applications. There has been much work in the field of\nMyanmar Optical Character Recognition (OCR) in recent decades. This study prepares the handwritten digit\nand character recognition dataset to train the model. The images in the dataset were arranged with black and\nwhite color space of 36 x 36 pixels standardized in size. This research proposed the Myanmar handwritten\ndigit and character recognition system by using the blocked features and Random Forest Classifier. The\nrecognition rates of Myanmar digits and characters are increased to 96.8% and 92.6% respectively with\nRandom Forest Classifier"}]}, "item_1583103108160": {"attribute_name": "Keywords", "attribute_value_mlt": [{"interim": "myanmar handwritten digits and characters, blocked features, random forest classifiers"}]}, "item_1583103120197": {"attribute_name": "Files", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_access", "date": [{"dateType": "Available", "dateValue": "2021-01-12"}], "displaytype": "preview", "download_preview_message": "", "file_order": 0, "filename": "Myanmar Handwritten Digit and Character Recognition Using.pdf", "filesize": [{"value": "701 KB"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_0", "mimetype": "application/pdf", "size": 701000.0, "url": {"url": "https://meral.edu.mm/record/7422/files/Myanmar Handwritten Digit and Character Recognition Using.pdf"}, "version_id": "98c89c2d-c6ad-408e-916e-29f4297933ac"}]}, "item_1583103131163": {"attribute_name": "Journal articles", "attribute_value_mlt": [{"subitem_issue": "No-1", "subitem_journal_title": "Scientific Journal of Innovative Research", "subitem_pages": "38-43", "subitem_volume": "Vol-2"}]}, "item_1583105942107": {"attribute_name": "Authors", "attribute_value_mlt": [{"subitem_authors": [{"subitem_authors_fullname": "Myint San"}, {"subitem_authors_fullname": "Phyu Phyu Khaing"}, {"subitem_authors_fullname": "Moe Thida Naing"}]}]}, "item_1583108359239": {"attribute_name": "Upload type", "attribute_value_mlt": [{"interim": "Publication"}]}, "item_1583108428133": {"attribute_name": "Publication type", "attribute_value_mlt": [{"interim": "Journal article"}]}, "item_1583159729339": {"attribute_name": "Publication date", "attribute_value": "2020-06-12"}, "item_title": "Myanmar Handwritten Digit and Character Recognition Using Blocked Features and Random Forest Classifier", "item_type_id": "21", "owner": "71", "path": ["1582963674932", "1597397050488"], "permalink_uri": "http://hdl.handle.net/20.500.12678/0000007422", "pubdate": {"attribute_name": "Deposited date", "attribute_value": "2021-01-12"}, "publish_date": "2021-01-12", "publish_status": "0", "recid": "7422", "relation": {}, "relation_version_is_last": true, "title": ["Myanmar Handwritten Digit and Character Recognition Using Blocked Features and Random Forest Classifier"], "weko_shared_id": -1}