Advertisement

Bangla Handwritten Digit Recognition Using Convolutional Neural Network

  • AKM Shahariar Azad Rabby
  • Sheikh AbujarEmail author
  • Sadeka Haque
  • Syed Akhter Hossain
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 755)

Abstract

Handwritten digit recognition has always a big challenge due to its variation of shape, size, and writing style. Accurate handwritten recognition is becoming more thoughtful to the researchers for its educational and economic values. There had several works been already done on the Bangla Handwritten Recognition, but still there is no robust model developed yet. Therefore, this paper states development and implementation of a lightweight CNN model for classifying Bangla Handwriting Digits. The proposed model outperforms any previous implemented method with fewer epochs and faster execution time. This Model was trained and tested with ISI handwritten character database Bhattacharya and Chaudhuri (IEEE Trans Pattern Anal Mach Intell 31:444–457, 2009, [1], BanglaLekha Isolated Biswas et al. (Data Brief 12, 103–107, 2017, [2]) and CAMTERDB 3.1.1 Sarkar et al. (Int J Doc Anal Recogn (IJDAR) 15(1):71–83, 2012, [3]). As a result, it was successfully achieved validation accuracy of 99.74% on ISI handwritten character database, 98.93% on BanglaLekha Isolated, 99.42% on CAMTERDB 3.1.1 dataset and lastly 99.43% on a mixed (combination of BanglaLekha Isolated, CAMTERDB 3.1.1 and ISI handwritten character dataset) dataset. This model achieved the best performance on different datasets and found very lightweight, it can be used on a low processing device like-mobile phone.The pre-train model and code for all these datasets can be found on this link https://github.com/shahariarrabby/Bangla_Digit_Recognition_CNN.

Keywords

Bangla handwritten recognition Convolutional neural network Pattern recognition Deep learning Computer vision Machine learning 

References

  1. 1.
    Bhattacharya, U., Chaudhuri, B.: Handwritten numeral databases of indian scripts and multistage recognition of mixed numerals. IEEE Trans. Pattern Anal. Mach. Intell. 31, 444–457 (2009).  https://doi.org/10.1109/TPAMI.2008.88CrossRefGoogle Scholar
  2. 2.
    Biswas, M., Islam, R., Gautam, K.S., Shopon, Md., Mohammed, N., Momen, S., Abedin, A.: BanglaLekha-Isolated: a multi-purpose comprehensive dataset of Handwritten Bangla Isolated characters. Data Brief. 12, 103–107 (2017).  https://doi.org/10.1016/j.dib.2017.03.035
  3. 3.
    Sarkar, R., Das, N., Basu, S., Kundu, M., Nasipuri, M., Basu, D.K.: Cmaterdb1: a database of unconstrained handwritten Bangla and Bangla-English mixed script document image. Int. J. Doc. Anal. Recogn. (IJDAR) 15(1), 71–83 (2012)CrossRefGoogle Scholar
  4. 4.
    LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. In: Proceedings of the IEEE, vol. 86(11), pp. 2278–2324, Nov 1998Google Scholar
  5. 5.
    Pal, U., Chaudhuri, B.: Automatic Recognition of Unconstrained Off-Line Bangla Handwritten Numerals 1948, 371–378 (2000).  https://doi.org/10.1007/3-540-40063-x_49
  6. 6.
    Alom, Md.Z., Sidike, P., Tarek, M.T., Asari, V.: Handwritten Bangla Digit Recognition using Deep Learning (2017)Google Scholar
  7. 7.
    Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization, Dec 2014. arXiv:1412.6980
  8. 8.
    Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15 1929–1958 (2014)Google Scholar
  9. 9.
    Janocha, K., Czarnecki, W.M.: On Loss Functions for Deep Neural Networks in Classification (2017). arXiv:1702.05659
  10. 10.
    Schaul, T., Zhang, S., and LeCun, Y.: No More Pesky Learning Rates (2012). arXiv:1206.1106
  11. 11.
    Khan, H.A., Helal, A.A., Ahmed, K.I.: Handwritten Bangla digit recognition using sparse representation classifier. In: 2014 International Conference on Informatics, Electronics & Vision (ICIEV), pp. 1–6. IEEE (2014)Google Scholar
  12. 12.
    Wen, Y., He, L.: A classifier for Bangla handwritten numeral recognition. Expert Syst. Appl. 39(1), 948–953 (2012)CrossRefGoogle Scholar
  13. 13.
    Hassan, T., Khan, H.A.: Handwritten Bangla numeral recognition using local binary pattern. In: 2015 International Conference on Electrical Engineering and Information Communication Technology (ICEEICT), pp. 1–4. IEEE (2015)Google Scholar
  14. 14.
    Nasir, M.K., Uddin, M.S.: Handwritten Bangla numerals recognition for automated postal system. IOSR J. Comput. Eng. 8(6), 43–48 (2013)CrossRefGoogle Scholar
  15. 15.
    Sarkhel, R., Das, N., Saha, A.K., Nasipuri, M.: A multi-objective approach towards cost-effective isolated handwritten Bangla character and digit recognition. Pattern Recogn. 58, 172–189 (2016)CrossRefGoogle Scholar
  16. 16.
    Mahbubar Rahman, S.I.P.S. Md., Akhand, M.A.H., Rahman, M.M.H.: Bangla handwritten character recognition using convolutional neural network. I. J. Image Graph. Signal Process. (IJIGSP) 7(3), 42–49 (2015)Google Scholar
  17. 17.
    Basu, S., Sarkar, R., Das, N., Kundu, M., Nasipuri, M., Basu, D.K.: Handwritten Bangla digit recognition using classifier combination through ds technique. In: Pattern Recognition and Machine Intelligence, pp. 236–241. Springer (2005)Google Scholar
  18. 18.
    Sharif, S.A.M., Nabeel, M., Mansoor, N., Momen, S.: A hybrid deep model with HOG features for Bangla handwritten numeral classification. In: 2016 9th International Conference on Electrical and Computer Engineering (ICECE), pp. 463–466 (2016)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • AKM Shahariar Azad Rabby
    • 1
  • Sheikh Abujar
    • 1
    Email author
  • Sadeka Haque
    • 1
  • Syed Akhter Hossain
    • 1
  1. 1.Department of Computer Science and EngineeringDaffodil International UniversityDhakaBangladesh

Personalised recommendations