Handwritten Bangla Character Recognition Using the State-of-the-Art Deep Convolutional Neural Networks

Computational Intelligence and Neuroscience
Md Zahangir AlomVijayan K Asari

Abstract

In spite of advances in object recognition technology, handwritten Bangla character recognition (HBCR) remains largely unsolved due to the presence of many ambiguous handwritten characters and excessively cursive Bangla handwritings. Even many advanced existing methods do not lead to satisfactory performance in practice that related to HBCR. In this paper, a set of the state-of-the-art deep convolutional neural networks (DCNNs) is discussed and their performance on the application of HBCR is systematically evaluated. The main advantage of DCNN approaches is that they can extract discriminative features from raw data and represent them with a high degree of invariance to object distortions. The experimental results show the superior performance of DCNN models compared with the other popular object recognition approaches, which implies DCNN can be a good candidate for building an automatic HBCR system for practical applications.

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Citations

Jul 27, 2021·PeerJ. Computer Science·Mir Moynuddin Ahmed ShiblyShamim Ripon
Mar 20, 2020··Mohammad Shorif UddinMohammad Reduanul Haque

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Methods Mentioned

BETA
feature extraction

Software Mentioned

VGG
Theano
Linux
All
Keras
VGG Net
GoogleNet
DenseNet
Conv
ResNet

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