• Md. Hadiuzzaman Bappy Computer Science and Engineering Discipline, Khulna University, Khulna, Bangladesh-9208
  • Md. Siamul Haq Computer Science and Engineering Discipline, Khulna University, Khulna, Bangladesh-9208
  • Kamrul Hasan Talukder Computer Science and Engineering Discipline, Khulna University, Khulna, Bangladesh-9208



Bangla Handwritten, Numeral Recognition, Optical Character Recognition (OCR), Deep CNN, Computer Vision, Machine Learning


The recognition of Bangla handwritten numerals (BHNR) has recently emerged as a very interesting area for machine learning and pattern recognition research. Recently, the technology for character or object recognition has also advanced. Bangla handwritten number recognition can serve as a foundation for creating an Optical Character Recognition (OCR) in the Bangla language. However, the lack of a sizable and accurate dataset makes Bangla's handwritten numeral recognition study insufficient in contrast to that of other well-known languages. Similar to MNIST for English digits, NumtaDB is by far the largest dataset collection for handwritten digits in the Bangla language. The most used datasets for the recognition of Bangla handwritten numerals in the past were NumtaDB, CMATERdb, and ISI. The majority of approaches now in use rely on feature extraction and a few outdated machine learning algorithms. Although some approaches operate quickly enough to meet practical demands, they are not always accurate. Even while certain techniques work quite well for languages other than Bangla, they still require improvement. Convolutional Neural Networks (CNN), in particular, are demonstrating excellent achievements in this discipline with higher accuracy. In this work, we’ve used custom CNN architectures to build our model to recognize digits using all the existing datasets with a high degree of accuracy. Our CNN model shows an average of 98% accuracy recognizing Bangla numeric in respect of above datasets. We have cross verified our model with mixed datasets and the result is also promising.


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Chaudhuri, B. B., & Pal, U. (1998). A complete printed Bangla OCR system. Pattern recognition, 31(5), 531-549.

Pal, U., & Chaudhuri, B. B. (1994). OCR in Bangla: an Indo-Bangladeshi language. In Proceedings of the 12th IAPR International Conference on Pattern Recognition, Vol. 3-Conference C: Signal Processing (Cat. No. 94CH3440-5), 2, 269-273.

Alam, S., Reasat, T., Doha, R. M., & Humayun, A. I. (2018). Numtadb-assembled bengali handwritten digits. arXiv preprint arXiv:1806.02452.

Kader, M. F., & Deb, K. (2012). Neural network-based English Alphanumeric character recognition. International Journal of Computer Science, Engineering and Applications, 2(4), 1.

Kakkar, P., & Dutta, U. (2014). A novel Approach to Recognition of English Characters Using Artificial Neural Networks. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, 3(6).

Prasad, K., Nigam, D. C., Lakhotiya, A., & Umre, D. (2013). Character recognition using matlab’s neural network toolbox. International Journal of u-and e-Service, Science and Technology, 6(1), 13-20.

Alcorn, T. M., & Hoggar, C. W. (1969). Pre-processing of data for character recognition. Marconi Review, 32(172), 61.

[Online]. Available:,q_auto:best/v1547672259 /3_qwv5gr.png [Accessed June 12, 2019].

[Online]. Available:*SGPGG7oeSvVlV5sOSQ2iZw.png [Accessed June 17, 2019].

Mahmoud, S. A., & Olatunji, S. O. (2009). Automatic recognition of off-line handwritten Arabic (Indian) numerals using support vector and extreme learning machines. International Journal of Imaging, 2(A09), 34-53.

Huang, Y. S., & Suen, C. Y. (1993). The behavior-knowledge space method for combination of multiple classifiers. In IEEE computer society conference on computer vision and pattern recognition, 347-347.

Wen, Y., Lu, Y., & Shi, P. (2007). Handwritten Bangla numeral recognition system and its application to postal automation. Pattern recognition, 40(1), 99-107.

Nabawi, A. A. F., & Mahmoud, S. A. (2000). Arabic optical text recognition: a classified bibliography. ERJ. Engineering Research Journal, 23(1), 79-131.

Liu, C. L., & Suen, C. Y. (2009). A new benchmark on the recognition of handwritten Bangla and Farsi numeral characters. Pattern Recognition, 42(12), 3287-3295.

“OpenCV” [Online]. Available: [Accessed Jan. 7, 2021].

“Off-Line Handwritten Bangla Numeral Dataset.” [Online]. Available: [Accessed August 7, 2019].

“CMATERdb 3.1.1: Handwritten Numeral Database.” [Online]. Available: http:// code. [Accessed August 13, 2019]

Paul, O. (2018). Image pre-processing on NumtaDB for Bengali handwritten digit recognition. In 2018 International Conference on Bangla Speech and Language Processing (ICBSLP), 1-6.

Islam, K. M., Noor, R., Saha, C., & Rahimi, J. (2018). A Deep Convolutional Neural Network for Bangla Handwritten Numeral Recognition. In 2018 IEEE International WIE Conference on Electrical and Computer Engineering (WIECON-ECE), 45-50.

Khan, H. A., Al Helal, A., & Ahmed, K. I. (2014). Handwritten bangla digit recognition using sparse representation classifier. In 2014 International Conference on Informatics, Electronics & Vision (ICIEV), 1-6.

Hassan, T., & Khan, H. A. (2015). Handwritten bangla numeral recognition using local binary pattern. In 2015 international conference on electrical engineering and information communication technology (ICEEICT), 1-4.

Das, N., Sarkar, R., Basu, S., Kundu, M., Nasipuri, M., & Basu, D. K. (2012). A genetic algorithm based region sampling for selection of local features in handwritten digit recognition application. Applied Soft Computing, 12(5), 1592-1606.

Sarkhel, R., Das, N., Saha, A. K., & Nasipuri, M. (2016). A multi-objective approach towards cost effective isolated handwritten Bangla character and digit recognition. Pattern Recognition, 58, 172-189.

Alom, M. Z., Sidike, P., Taha, T. M., & Asari, V. K. (2017). Handwritten bangla digit recognition using deep learning. arXiv preprint arXiv:1705.02680.

Boni, P. K., Abir, B. S., Hasan, H. M., & Islam, M. R. (2018). Handwritten bangla digit recognition using chemical reaction optimization. In 2018 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT), 1-7.

Roy, A., Mazumder, N., Das, N., Sarkar, R., Basu, S., & Nasipuri, M. (2012). A new quad tree based feature set for recognition of handwritten bangla numerals. In 2012 IEEE international conference on engineering education: Innovative practices and future trends (AICERA), 1-6.

Shopon, M., Mohammed, N., & Abedin, M. A. (2016). Bangla handwritten digit recognition using autoencoder and deep convolutional neural network. In 2016 International Workshop on Computational Intelligence (IWCI), 64-68.




How to Cite

M. H. . Bappy, M. S. . Haq, and K. H. . Talukder, “BANGLA HANDWRITTEN NUMERAL RECOGNITION USING DEEP CONVOLUTIONAL NEURAL NETWORK”, Khulna Univ. Stud., pp. 863–877, Nov. 2022.

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