Mamta Bisht
Department of Electronics and Communication Engineering, Jaypee Institute of Information Technology, Noida, India.

Richa Gupta
Department of Electronics and Communication Engineering, Jaypee Institute of Information Technology, Noida, India.

DOI https://doi.org/10.33889/IJMEMS.2020.5.6.106

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Abstract

The handwriting style of every writer consists of variations, skewness and slanting nature and therefore, it is a stimulating task to recognise these handwritten documents. This article presents a study on various methods available in literature for Devanagari handwritten character recognition and performs its implementation using Convolutional neural network (CNN). Available methods are studied on different parameters and a tabular comparison is also presented which concludes superiority of CNN model in character recognition task. The proposed CNN model results in well acceptable accuracy using dropout and stochastic gradient descent (SGD) optimizer.

Keywords- Optical character recognition (OCR), Devanagari script, Numeral recognition, Character recognition, Convolutional neural network.

Citation

Bisht, M., & Gupta, R. (2020). Multiclass Recognition of Offline Handwritten Devanagari Characters using CNN. International Journal of Mathematical, Engineering and Management Sciences, 5(6), 1429-1439. https://doi.org/10.33889/IJMEMS.2020.5.6.106.