Abstract
Automatic handwriting recognition is challenging task due to its sheer variety of acceptable stylistic differences. This is especially true for scripts with large character sets. Bangla, the sixth most widely spoken language in the world has a complex, large and rich set of compound characters. In this study, a hybrid deep learning model is proposed which combines the use of the manually designed feature Histogram of Oriented Gradients (HOG), with the adaptively learned features of a Convolutional Neural Networks (CNN). The proposed hybrid model was trained on the CMATERDB 3.1.3.3, a Bangla compound character data set which divides Bangla compound characters into 177 broad classes and 199 specific classes. The results demonstrate that CNN-only models achieve over 91% and 92% test accuracy respectively. Furthermore, it is shown that the proposed model, which incorporates HOG features with a CNN, achieves over 92.50% test accuracy on each division. While there is still room for improvement, these results are significantly better than currently published state of art on this data set.
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
Chatfield, K., Lempitsky, V.S., Vedaldi, A., Zisserman, A.: The devil is in the details: an evaluation of recent feature encoding methods. In: BMVC. vol. 2, p. 8 (2011)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on. vol. 1, pp. 886–893. IEEE (2005)
Das, N., Acharya, K., Sarkar, R., Basu, S., Kundu, M., Nasipuri, M.: A novel ga-svm based multistage approach for recognition of handwritten bangla compound characters. In: Proceedings of the International Conference on Information Systems Design and Intelligent Applications 2012 (INDIA 2012) held in Visakhapatnam, India, January 2012. pp. 145–152. Springer (2012)
Das, N., Acharya, K., Sarkar, R., Basu, S., Kundu, M., Nasipuri, M.: A benchmark image database of isolated bangla handwritten compound characters. International Journal on Document Analysis and Recognition (IJDAR) 17(4), 413–431 (2014)
Das, N., Basu, S., Sarkar, R., Kundu, M., Nasipuri, M., Basu, D.: Handwritten bangla compound character recognition: Potential challenges and probable solution. In: IICAI. pp. 1901–1913 (2009)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Pereira, F., Burges, C.J.C., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems 25, pp. 1097–1105. Curran Associates, Inc. (2012)
LeCun, Y., Bengio, Y.: Convolutional networks for images, speech, and time series. The handbook of brain theory and neural networks 3361(10), 1995 (1995)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International journal of computer vision 60(2), 91–110 (2004)
M. A. H. Akhand, Mahtab Ahmad, M.M.H.R.: Convolutional neural network training with artificial pattern for bangla handwritten numeral recognition. ICIEB 1(1), 1–6 (2016)
Maitra, D.S., Bhattacharya, U., Parui, S.K.: Cnn based common approach to handwritten character recognition of multiple scripts. In: Document Analysis and Recognition (ICDAR), 2015 13th International Conference on. pp. 1021–1025. IEEE (2015)
Sharif Razavian, A., Azizpour, H., Sullivan, J., Carlsson, S.: Cnn features off-the-shelf: an astounding baseline for recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. pp. 806–813 (2014)
Srivastava, N., Hinton, G.E., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. Journal of Machine Learning Research 15(1), 1929–1958 (2014)
Zeiler, M.D.: Adadelta: an adaptive learning rate method. arXiv preprint arXiv:1212.5701 (2012)
Acknowledgements
This work was supported by the ICT division of Ministry of ICT, Bangladesh [Grant number 56.00.0000.028.33.066.16-731].
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Sharif, S.M.A., Mohammed, N., Momen, S., Mansoor, N. (2018). Classification of Bangla Compound Characters Using a HOG-CNN Hybrid Model. In: Mandal, J., Saha, G., Kandar, D., Maji, A. (eds) Proceedings of the International Conference on Computing and Communication Systems. Lecture Notes in Networks and Systems, vol 24. Springer, Singapore. https://doi.org/10.1007/978-981-10-6890-4_39
Download citation
DOI: https://doi.org/10.1007/978-981-10-6890-4_39
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-6889-8
Online ISBN: 978-981-10-6890-4
eBook Packages: EngineeringEngineering (R0)