Document Type
Article
Publication Date
2018
Publication Source
Computational Intelligence and Neuroscience
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.
ISBN/ISSN
1687-5265
Document Version
Published Version
Publisher
Hindawi LTD
Volume
2018
Peer Reviewed
yes
eCommons Citation
Alom, Md Zahangir; Sidike, Paheding; Hasan, Mahmudul; Taha, Tarek M.; and Asari, Vijayan K., "Handwritten Bangla Character Recognition Using the State-of-the-Art Deep Convolutional Neural Networks" (2018). Electrical and Computer Engineering Faculty Publications. 465.
https://ecommons.udayton.edu/ece_fac_pub/465
Included in
Computer Engineering Commons, Electrical and Electronics Commons, Electromagnetics and Photonics Commons, Optics Commons, Other Electrical and Computer Engineering Commons, Systems and Communications Commons
Comments
This open-access article is provided for download in compliance with the publisher’s policy on self-archiving. To view the version of record, use the DOI: https://doi.org/10.1155/2018/6747098