2026-04-16T15:56:57Z https://meral.edu.mm/oai

oai:meral.edu.mm:recid/5289 2022-03-24T23:12:08Z 1582963413512:1596119372420 user-ytu

Transfer Learning Based Myanmar Sign Language Recognition for Myanmar Consonants Ni Htwe Aung Ye Kyaw Thu Su Su Maung Swe Zin Moe Hlaing Myat Nwe 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. 2020-04-30 http://hdl.handle.net/20.500.12678/0000005289 https://meral.edu.mm/records/5289