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dc.contributor.authorYadav, Madhuri
dc.contributor.authorKr Purwar, Ravindra
dc.contributor.authorJain, Anchal
dc.identifier.citationADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 7 (2018)
dc.description.abstractHandwritten character recognition is a challenging problem which received attention because of its potential benefits in real-life applications. It automates manual paper work, thus saving both time and money, but due to low recognition accuracy it is not yet practically possible. This work achieves higher recognition rates for handwritten isolated characters using Deep learning based Convolutional neural network (CNN). The architecture of these networks is complex and plays important role in success of character recognizer, thus this work experiments on different CNN architectures, investigates different optimization algorithms and trainable parameters. The experiments are conducted on two different types of grayscale datasets to make this work more generic and robust. One of the CNN architecture in combination with adadelta optimization achieved a recognition rate of 97.95%. The experimental results demonstrate that CNN based end-to-end learning achieves recognition rates much better than the traditional techniques.
dc.publisherEdiciones Universidad de Salamanca (España)
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Unported
dc.subjectInformation Technology
dc.titleDesign of CNN architecture for Hindi Characters

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Attribution-NonCommercial-NoDerivs 3.0 Unported
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 Unported