Abstract
Handwritten Character Recognition in Indian scripts is a complex problem compared to printed characters. This paper concerns the handwritten isolated Devanagari characters, mainly consonants of 36 classes. The dataset for Devanagari Characters available at UCI library is chosen for this work which consists of 72,000 images for classification. CNN, Alexnet, LeNet and modified LeNet, are used for classification of this dataset. While CNN achieved an accuracy of 98.63% for the training data and 97.71% for unseen data, Alexnet has shown an accuracy of 98.71% accuracy for unseen data and 96.78% for training data. Experiments on LeNet and Modified LeNet have compromised the results with 87.76% maximum. Deep Learning algorithms were analysed with different training and testing data split (80:20, 70:30 and 90:10) with varying combinations of epochs (40, 50, 150).
S. Kumaran and P. R. Kamath—These authors contributed equally to this work.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ahmed, R., et al.: Offline Arabic handwriting recognition using deep machine learning: a review of recent advances. In: Ren, J., et al. (eds.) BICS 2019. LNCS (LNAI), vol. 11691, pp. 457–468. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-39431-8_44
Yadav, M., Purwar, R.K., Mittal, M.: Handwritten Hindi character recognition: a review. IET Image Proc. 12(11), 1919–1933 (2018)
Kim, I.-J., Xie, X.: Handwritten hangul recognition using deep convolutional neural networks. Int. J. Doc. Anal. Recogn. (IJDAR) 18(1), 1–13 (2015)
Latif, G., Alghazo, J., Alzubaidi, L., Naseer, M.M., Alghazo, Y.: Deep convolutional neural network for recognition of unified multi-language handwritten numerals. In: 2018 IEEE 2nd International Workshop on Arabic and Derived Script Analysis and Recognition (ASAR), pp. 90–95. IEEE (2018)
Shima, Y., Nakashima, Y., Yasuda, M.: Handwritten digits recognition by using CNN Alex-Net pre-trained for large-scale object image dataset. In: Proceedings of the 3rd International Conference on Multimedia Systems and Signal Processing, pp. 36–40 (2018)
Ram, S., Gupta, S., Agarwal, B.: Devanagri character recognition model using deep convolution neural network. J. Stat. Manag. Syst. 21(4), 593–599 (2018)
Li, Z., Teng, N., Jin, M., Lu, H.: Building efficient CNN architecture for offline handwritten Chinese character recognition. Int. J. Doc. Anal. Recogn. (IJDAR) 21(4), 233–240 (2018)
Jangid, M., Srivastava, S.: Handwritten Devanagari character recognition using layer-wise training of deep convolutional neural networks and adaptive gradient methods. J. Imaging 4(2), 41 (2018)
Rojatkar, D.V., Chinchkhede, K.D., Sarate, G.: Handwritten Devnagari consonants recognition using MLPNN with five fold cross validation. In: 2013 International Conference on Circuits, Power and Computing Technologies (ICCPCT), pp. 1222–1226. IEEE (2013)
Abdelazeem, S.: Comparing Arabic and Latin handwritten digits recognition problems. Int. J. Comput. Inf. Eng. 3(6), 1583–1587 (2009)
Deng, L.: The MNIST database of handwritten digit images for machine learning research [best of the web]. IEEE Signal Process. Mag. 29(6), 141–142 (2012)
Khosravi, H., Kabir, E.: Introducing a very large dataset of handwritten Farsi digits and a study on their varieties. Pattern Recogn. Lett. 28(10), 1133–1141 (2007)
Acharya, S., Pant, A.K., Gyawali, P.K.: Deep learning based large scale handwritten Devanagari character recognition. In: 2015 9th International Conference on Software, Knowledge, Information Management and Applications (SKIMA), pp. 1–6. IEEE (2015)
Prashanth, D.S., Mehta, R., Ramana, K., Bhaskar, V.: Handwritten Devanagari character recognition using modified LeNet and AlexNet convolution neural networks. Wirel. Pers. Commun. 122(1), 349–378 (2022)
Prashanth, D.S., Mehta, R.V.K., Challa, N.P.: A multi-purpose dataset of Devanagari script comprising of isolated numerals and vowels. Data Brief 40, 107723 (2022)
Acknowledgements
The authors thank the college authorities of SCSVMV University, Sahyadri College of Engineering & Management, Centre of Excellence in AI & ML in Sahyadri College of Engineering & Management, for their support.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Ethics declarations
Conflict of Interests
The authors declare that they have no known competing for financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Duddela, S.P., Kumaran, S., Kamath, P.R. (2023). Analysis on Classification of Handwritten Devanagari Characters Using Deep Learning Models. In: Prabhu, S., Pokhrel, S.R., Li, G. (eds) Applications and Techniques in Information Security . ATIS 2022. Communications in Computer and Information Science, vol 1804. Springer, Singapore. https://doi.org/10.1007/978-981-99-2264-2_18
Download citation
DOI: https://doi.org/10.1007/978-981-99-2264-2_18
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-2263-5
Online ISBN: 978-981-99-2264-2
eBook Packages: Computer ScienceComputer Science (R0)