{"created":"2020-09-08T09:15:18.606999+00:00","id":5289,"links":{},"metadata":{"_buckets":{"deposit":"41b408f1-5784-4549-ad4a-05d4dbe4990c"},"_deposit":{"created_by":30,"id":"5289","owner":"30","owners":[30],"owners_ext":{"displayname":"","email":"khinmohmohaung@gmail.com","username":""},"pid":{"revision_id":0,"type":"recid","value":"5289"},"status":"published"},"_oai":{"id":"oai:meral.edu.mm:recid/5289","sets":["1582963413512:1596119372420"]},"communities":["ytu"],"item_1583103067471":{"attribute_name":"Title","attribute_value_mlt":[{"subitem_1551255647225":"Transfer Learning Based Myanmar Sign Language Recognition for Myanmar Consonants","subitem_1551255648112":"en"}]},"item_1583103085720":{"attribute_name":"Description","attribute_value_mlt":[{"interim":"Abstract— In this paper, a study on the different Transfer Learning models is made for the purpose\nof recognizing Myanmar Fingerspelling (Myanmar Sign Language) alphabets. This experiment shows\nthat Transfer Learning can play a significant role for sign language recognition system and is capable of\nrecognizing the static hand gesture images that represent the Myanmar consonants from က (ka) to အ (a).\nThe main objective of this paper is to investigate the performance of various Transfer Learning models\nfor Myanmar Fingerspelling recognition. We proposed 12 Transfer Learning models using TensorFlow\nlibrary and the accuracy for each model is compared. Among these 12 models, VGG16, ResNet50\nand MobileNet with epoch 50 yielded the highest accuracy score with 94%. Although there are some\nlimitations in the datasets, each model provides the encouraging results and thus, it can believe that\nthe fully generalizable recognition system based on Transfer Learning can be produced for all Myanmar\nSign Language Fingerspelling characters by doing further research with more data."}]},"item_1583103108160":{"attribute_name":"Keywords","attribute_value_mlt":[{"interim":"Myanmar Sign Language"},{"interim":"Myanmar Fingerspelling"},{"interim":"Transfer Learning"},{"interim":"Myanmar consonants"}]},"item_1583103120197":{"attribute_name":"Files","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_access","date":[{"dateType":"Available","dateValue":"2020-09-08"}],"displaytype":"preview","filename":"Transfer Learning Based Myanmar Sign Language.pdf","filesize":[{"value":"2.9 Mb"}],"format":"application/pdf","licensetype":"license_0","url":{"url":"https://meral.edu.mm/record/5289/files/Transfer Learning Based Myanmar Sign Language.pdf"},"version_id":"c03c2444-c7e2-4a4f-a623-a86c68b44c77"}]},"item_1583103131163":{"attribute_name":"Journal articles","attribute_value_mlt":[{"subitem_issue":"1","subitem_journal_title":"Journal of Intelligent Informatics and Smart Technology","subitem_pages":"pp 35-44","subitem_volume":"4"}]},"item_1583105942107":{"attribute_name":"Authors","attribute_value_mlt":[{"subitem_authors":[{"subitem_authors_fullname":"Ni Htwe Aung"},{"subitem_authors_fullname":"Ye Kyaw Thu"},{"subitem_authors_fullname":"Su Su Maung"},{"subitem_authors_fullname":"Swe Zin Moe"},{"subitem_authors_fullname":"Hlaing Myat Nwe"}]}]},"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":"2020-04-30"},"item_title":"Transfer Learning Based Myanmar Sign Language Recognition for Myanmar Consonants","item_type_id":"21","owner":"30","path":["1596119372420"],"publish_date":"2020-09-08","publish_status":"0","recid":"5289","relation_version_is_last":true,"title":["Transfer Learning Based Myanmar Sign Language Recognition for Myanmar Consonants"],"weko_creator_id":"30","weko_shared_id":-1},"updated":"2022-03-24T23:12:08.724010+00:00"}