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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/5588
Title: Handwritten Bangla character and numeral recognition using convolutional neural network for low-memory GPU
Authors: Keserwani P.
Ali T.
Pratim Roy, Partha
Published in: International Journal of Machine Learning and Cybernetics
Abstract: In this work, a convolutional neural network (CNN) based architecture is proposed for low memory GPU to recognize the handwritten isolated Bangla characters and numerals. The merit of the proposed architecture is the lesser number of trainable parameters as compared to the standard deep architectures and enabling it to train the proposed architecture on the low-memory GPU. The features from various layers of CNN are fused to handle the multi-scale nature of a character. The spatial pyramid pooling on the fused features produces a fixed size feature vector. It helps to reduce the number of parameters of the proposed model. Extensive experiments have been conducted on various versions of publicly available Bangla character dataset CMATERdb. The proposed architecture yields competitive results as compared to the fine-tuned standard deep architectures such as AlexNet, VGGNet, and GoogLeNet. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.
Citation: International Journal of Machine Learning and Cybernetics (2019), 10(12): 3485-3497
URI: https://doi.org/10.1007/s13042-019-00938-1
http://repository.iitr.ac.in/handle/123456789/5588
Issue Date: 2019
Publisher: Springer
Keywords: Bangla characters and numerals
Convolutional neural network
Low-memory GPU
ISSN: 18688071
Author Scopus IDs: 57205562856
55314607400
56880478500
Author Affiliations: Keserwani, P., Department of Computer Science and Engineering, Indian Institute of Technology Roorkee, Roorkee, India
Ali, T., Department of Computer Science and Engineering, Indian Institute of Technology Roorkee, Roorkee, India
Roy, P.P., Department of Computer Science and Engineering, Indian Institute of Technology Roorkee, Roorkee, India
Corresponding Author: Keserwani, P.; Department of Computer Science and Engineering, Indian Institute of Technology RoorkeeIndia; email: prateekeserwani@gmail.com
Appears in Collections:Journal Publications [CS]

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