@INPROCEEDINGS{hye-lstm,
author={M. {Rahat-uz-Zaman} and S. {Hye}},
booktitle={2020 IEEE Region 10 Symposium (TENSYMP), 5-7 June 2020, Dhaka, Bangladesh},
title={Extraction of Sequence from Bangla Handwritten Numerals and Recognition Using LSTM},
year={2020},
volume={},
number={},
pages={1-4},
abstract={In the promising era of Handwritten Numeral Recognition (HNR), despite Bangla being one of the major languages in the Indian subcontinent, fewer explorations have been done on Bangla numerals compared to other languages. Among the existing methods, several convolutional neural network (CNN) based method outperformed other methods. But CNN always gets confused with some specific Bangla numerals due to the similarity of shape and size of different numerals. The main purpose of this study is to expand Bangla HNR by considering a novel methodology with a Long Short-Term Memory (LSTM) network. In the proposed method, images are thinned and a sequence is extracted. These extracted sequences are used to classify using LSTM network. Both single-layer LSTM and Deep LSTM models are trained and performance tested on a benchmark dataset with a large number of samples. On the other hand, traditional CNN is also trained for better understanding. Experimental outcomes revealed that the proposed LSTM based method outperformed CNN with remarkable accuracy for the similar shaped numerals. Finally, the proposed method achieved a test set recognition rate of 98.03% which is better than or competitive to other prominent existing methods.},
keywords={Handwritten Numeral Recognition;Convolutional Neural Network;Long Short-Term Memory;Sequence Extraction},
doi={},
ISSN={},
month={Jun}
}