Handwritten Bangla Numerical Digit Recognition Using Fine Regulated Deep Neural Network

Authors

  • Md. Shahadat Hossain Pabna University of Science and Technology
  • Md. Anwar Hossain Pabna University of Science and Technology
  • AFM Zainul Abadin Pabna University of Science and Technology
  • Md. Manik Ahmed Pabna University of Science and Technology

DOI:

https://doi.org/10.18034/ei.v9i2.551

Keywords:

Bangla Handwritten Recognition, OCR, DCNN, TDNN, FRDNN

Abstract

The recognition of handwritten Bangla digit is providing significant progress on optical character recognition (OCR). It is a very critical task due to the similar pattern and alignment of handwriting digits. With the progress of modern research on optical character recognition, it is reducing the complexity of the classification task by several methods, a few problems encounter during recognition and wait to be solved with simpler methods. The modern emerging field of artificial intelligence is the Deep Neural Network, which promises a solid solution to these few handwritten recognition problems. This paper proposed a fine regulated deep neural network (FRDNN) for the handwritten numeric character recognition problem that uses convolutional neural network (CNN) models with regularization parameters which makes the model generalized by preventing the overfitting. This paper applied Traditional Deep Neural Network (TDNN) and Fine regulated deep neural network (FRDNN) models with a similar layer experienced on BanglaLekha-Isolated databases and the classification accuracies for the two models were 96.25% and 96.99%, respectively over 100 epochs. The network performance of the FRDNN model on the BanglaLekha-Isolated digit dataset was more robust and accurate than the TDNN model and depend on experimentation. Our proposed method is obtained a good recognition accuracy compared with other existing available methods.

Downloads

Download data is not yet available.

Author Biographies

Md. Shahadat Hossain, Pabna University of Science and Technology

Department of Information and Communication Engineering, Pabna University of Science and Technology, Pabna, BANGLADESH

Md. Anwar Hossain, Pabna University of Science and Technology

Department of Information and Communication Engineering, Pabna University of Science and Technology, Pabna, BANGLADESH

AFM Zainul Abadin, Pabna University of Science and Technology

Department of Information and Communication Engineering, Pabna University of Science and Technology, Pabna, BANGLADESH

Md. Manik Ahmed, Pabna University of Science and Technology

Department of Information and Communication Engineering, Pabna University of Science and Technology, Pabna, BANGLADESH

References

Ahmed, M., Akhand, M. A. H., & Rahman, M. M. Hafizur. (2019). Recognizing Bangla Handwritten Numeral Utilizing Deep Long Short-Term Memory. Int. J. Image, Graph. Signal Process, 11(1), 23–32.

Arbain, N. A., Azmi, M. S., Muda A. K., Muda, N. A. & Radzid, A. R. (2018). Offline handwritten digit recognition using triangle geometry properties. Int. J. Comput. Inf. Syst. Ind. Manag. Appl., 10(January), 87–97.

Biswas, M., Islam, R., Shom, G. K., Shopon, M., Mohammed, N., Momen, S., Abedin, A. (2017). BanglaLekha-Isolated: A multi-purpose comprehensive dataset of Handwritten Bangla Isolated characters. Data in Brief, 12, 103-107. https://doi.org/10.1016/j.dib.2017.03.035

Das, P., Dasgupta, T. & Bhattacharya, S. (2018). A Bengali handwritten vowels recognition scheme based on the detection of structural anatomy of the characters. Adv. Intell. Syst. Comput., 518(January), 245–252.

Hochuli, A. G., Oliveira, L. S., Britto, A. S., & Sabourin, R. (2018). Handwritten digit segmentation: Is it still necessary? Pattern Recognition, 78, 1–11.

Kamran, S. A., Humayun, A. I., Alam, S., Doha, R. M., Mandal, M. K., Reasat, T., & Rahman, F. (2018). AI Learns to Recognize Bengali Handwritten Digits: Bengali.AI Computer Vision Challenge 2018. https://arxiv.org/abs/1810.04452v1

Pramanik, R., & Bag, S. (2018). Shape decomposition-based handwritten compound character recognition for Bangla OCR. Journal of Visual Communication and Image Representation, Volume 50, 123-134.

Rahman M. M., Akhand, M. A. H., Islam, S., Shill, P. C. (2015). Bangla Handwritten Character Recognition using Convolutional Neural Network. I. J. Image, Graphics and Signal Processing, 8, 42-49.

Ramzan, M., Khan, H. U., Awan, S. M., Akhtar, W., Ilyas, M., Mahmood, A., & Zamir, A. (2018). A survey on using neural network based algorithms for hand written digit recognition. Int. J. Adv. Comput. Sci. Appl., 9(9), 519–528. https://doi.org/10.14569/IJACSA.2018.090965

Shamim, S. M., Miah, M. B. A., Sarker, A., Rana, M., & Jobair, A. (2018). Al. Handwritten digit recognition using machine learning algorithms. Indones. J. Sci. Technol., 3(1), 29–39.

Sharif, S. M. A., & Mahboob, M. (2019). Deep Hog: a Hybrid Model To Classify Bangla Isolated Alpha-Numerical Symbols. Neural Netw. World, 29(3), 111–133.

Thapa, R. & Kumar, D. (2018). Recognizing Digits from Natural Images and handwritten Digits using Deep Convolutional Neural Networks. International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), 4(1), 158-165.

--0--

Downloads

Published

2021-07-01

How to Cite

Hossain, M. S., Hossain, M. A., Abadin, A. Z., & Ahmed, M. M. (2021). Handwritten Bangla Numerical Digit Recognition Using Fine Regulated Deep Neural Network. Engineering International, 9(2), 73–84. https://doi.org/10.18034/ei.v9i2.551

Issue

Section

Peer Reviewed Articles