Recognizing Bangla Handwritten Numeral Utilizing Deep Long Short Term Memory

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Mahtab Ahmed 1,* M. A. H. Akhand 2 M. M. Hafizur Rahman 3

1. Dept. of CSE, Khulna University of Engineering & Technology, Khulna, Bangladesh

2. University of Western Ontario, Canada

3. Dept. of Communications and Networks, CCSIT, King Faisal University, Saudi Arabia

* Corresponding author.


Received: 13 Sep. 2018 / Revised: 27 Sep. 2018 / Accepted: 17 Oct. 2018 / Published: 8 Jan. 2019

Index Terms

Bangla Handwritten Numeral, Long Short Term Memory, Deep Neural Networks


Handwritten numeral recognition (HNR) has gained much attention in present days as it can be applied in range of applications. Research on unconstrained HNR has shown impressive progress in few scripts but is far behind for Bangla although it is one of the major languages. Bangla contains similar shaped numerals which are difficult to distinguish even in printed form and this makes Bangla HNR (BHNR) a challenging task. Our goal in this study is to build up a superior BHNR framework and consequently explore the profound design of Long Short Term Memory (LSTM) method. LSTM is a variation of Recurrent Neural Network and is effectively used for sequence ordering with its distinct features. This study considered deep architecture of LSTM for better performance. The proposed BHNR with deep LSTM (BNHR-DLSTM) standardizes the composed numeral images first and then utilizes two layers of LSTM to characterize singular numerals. Benchmark dataset with 22000 handwritten numerals having various shapes, sizes and varieties are utilized to examine the proficiency of BNHR-DLSTM. The proposed method indicates agreeable recognition precision and beat other conspicuous existing methods.

Cite This Paper

Mahtab Ahmed, M. A. H. Akhand, M. M. Hafizur Rahman, " Recognizing Bangla Handwritten Numeral Utilizing Deep Long Short Term Memory", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.11, No.1, pp. 23-32, 2019. DOI: 10.5815/ijigsp.2019.01.03


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