Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/2347
Title: Offline handwritten gurumukhi character recognition system using convolution neural network
Authors: Gupta S
Jindal U
Gupta D
Gupta R.
Keywords: CNN
CRS (Character Recognition System)
Deep Learning
Gurumukhi
Handwritten Script.
Issue Date: 2019
Publisher: American Scientific Publishers
Abstract: A lot of literature is available on the recognition of handwriting on scripts other than Indians, but the number of articles related to Indian scripts recognition such as Gurumukhi are much less. Gurumukhi is a religion-specific language that ranks 14th frequently spoken language in all languages of the world. In Gurumukhi script, some characters are alike to each other which makes recognition task very difficult. Therefore this article presents a novel approach for Gurumukhi character. This article lays emphasis on convolutional neural networks (CNN), which intend to obtain the features of given data samples and then its mapping is being performed to the right observation. In this approach, a dataset has been prepared for 10 Gurumukhi characters. The proposed methodology obtains a recognition accuracy of 99.34% on Gurumukhi characters images without making use of any post-processing method.
URI: 10.1166/jctn.2019.8497
http://hdl.handle.net/123456789/2347
Appears in Collections:Journals

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