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Erschienen in:
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2016 | OriginalPaper | Buchkapitel

Deep Convolutional Neural Network Classifier for Handwritten Devanagari Character Recognition

verfasst von : Pratibha Singh, Ajay Verma, Narendra S. Chaudhari

Erschienen in: Information Systems Design and Intelligent Applications

Verlag: Springer India

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Abstract

The performance of two architecture of Neural Networks are compared for handwritten Devanagari character recognition. The first one is the fully connected Feed-forward Neural Network and the second one is deep Convolutional Neural Network. Deep learning is basically a biologically inspired technique based on human brain. A part of brain called neocortex is having layered architecture. The advantage of using CNN is that it does not require complex preprocessing or feature extraction algorithm. Image pixels are the input for the two networks. We obtained the improved result for standard character benchmarking datasets.

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Vorheriges Kapitel Script Based Trilingual Handwritten Word Level Multiple Skew Estimation
Nächstes Kapitel A Clustering-Based Generic Interaction Protocol for Multiagent Systems
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Metadaten
Titel
Deep Convolutional Neural Network Classifier for Handwritten Devanagari Character Recognition
verfasst von
Pratibha Singh
Ajay Verma
Narendra S. Chaudhari
Copyright-Jahr
2016
Verlag
Springer India
Buch
Information Systems Design and Intelligent Applications

Print ISBN: 978-81-322-2750-2

Electronic ISBN: 978-81-322-2752-6

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-81-322-2752-6_54