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Transfer Learning Based Myanmar Sign Language Recognition for Myanmar Consonants

http://hdl.handle.net/20.500.12678/0000005289
b4cba743-fa4d-462c-8953-bf431fb193c0
41b408f1-5784-4549-ad4a-05d4dbe4990c
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Transfer Transfer Learning Based Myanmar Sign Language.pdf (2.9 Mb)
Publication type
Journal article
Upload type
Publication
Title
Title Transfer Learning Based Myanmar Sign Language Recognition for Myanmar Consonants
Language en
Publication date 2020-04-30
Authors
Ni Htwe Aung
Ye Kyaw Thu
Su Su Maung
Swe Zin Moe
Hlaing Myat Nwe
Description
Abstract— In this paper, a study on the different Transfer Learning models is made for the purpose
of recognizing Myanmar Fingerspelling (Myanmar Sign Language) alphabets. This experiment shows
that Transfer Learning can play a significant role for sign language recognition system and is capable of
recognizing the static hand gesture images that represent the Myanmar consonants from က (ka) to အ (a).
The main objective of this paper is to investigate the performance of various Transfer Learning models
for Myanmar Fingerspelling recognition. We proposed 12 Transfer Learning models using TensorFlow
library and the accuracy for each model is compared. Among these 12 models, VGG16, ResNet50
and MobileNet with epoch 50 yielded the highest accuracy score with 94%. Although there are some
limitations in the datasets, each model provides the encouraging results and thus, it can believe that
the fully generalizable recognition system based on Transfer Learning can be produced for all Myanmar
Sign Language Fingerspelling characters by doing further research with more data.
Keywords
Myanmar Sign Language, Myanmar Fingerspelling, Transfer Learning, Myanmar consonants
Journal articles
1
Journal of Intelligent Informatics and Smart Technology
pp 35-44
4
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