Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Book Chapter Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Handwritten digit recognition using convolutional neural networks Subject The topic of the resource Convolutional neural network; Deep neural network; Handwritten digit recognition; Multilayer perceptron; Optical character recognition; Recurrent neural network Description An account of the resource Optical character recognition (OCR) systems have been used for extraction of text contained in scanned documents or images. This system consists of two steps: character detection and recognition. One classification algorithm is required for character recognition by their features. Character can be recognized using neural networks. The multilayer perceptron (MLP) provides acceptable recognition accuracy for character classification. Moreover, the convolutional neural network (CNN) and the recurrent neural network (RNN) are providing character recognition with high accuracy. MLP, RNN, and CNN may suffer from the large amount of computation in the training phase. MLP solves different types of problems with good accuracy but it takes huge amount of time due to its dense network connection. RNNs are suitable for sequence data, while CNNs are suitable for spatial data. In this chapter, a CNN is implemented for recognition of digits from MNIST database and a comparative study is established between MLP, RNN, and CNN. The CNN provides the higher accuracy for digit recognition and takes lowest amount of time for training the system with respect to MLP and RNN. The CNN gives better result with accuracy up to 98.92% as the MNIST digit dataset is used, which is spatial data. 2020 Walter de Gruyter GmbH, Berlin/Boston. All rights reserved. Creator An entity primarily responsible for making the resource Jana R.; Bhattacharyya S.; Das S. Source A related resource from which the described resource is derived Deep Learning: Research and Applications, pp. 51-68. Publisher An entity responsible for making the resource available De Gruyter Mouton Date A point or period of time associated with an event in the lifecycle of the resource 2020-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1515/9783110670905-003" target="_blank" rel="noreferrer noopener">https://doi.org/10.1515/9783110670905-003</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092120901&amp;doi=10.1515%2F9783110670905-003&amp;partnerID=40&amp;md5=83457da4acc4e1debb6ceca1e4dff978" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092120901&amp;doi=10.1515%2f9783110670905-003&amp;partnerID=40&amp;md5=83457da4acc4e1debb6ceca1e4dff978</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 978-311067090-5; 978-311067079-0 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Book chapter Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Jana R., RCC Institute of Information Technology, Kolkata, West Bengal, India; Bhattacharyya S., CHRIST (Deemed to be University), Bangalore, Karnataka, India; Das S., Indian Statistical Institute, Kolkata, West Bengal, India