Advertisement

Alphanumeric Character Recognition on Tiny Dataset

  • Sujit S. AminEmail author
  • Lata Ragha
Conference paper
  • 37 Downloads
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 49)

Abstract

Alphanumeric character recognition is the ability of a computer to understand digits and alphabets image as an input. This topic has struggled to reach a wide variety of people since we need quite a lot of labeled training data. A small training sample achieves sub-standard accuracy in training data that is model is not able to learn accurately. Because of this handwritten (alphanumeric character), recognition is not widely been used. We resolve this issue by creating new labeled training data. Generated data is generated from existing samples, with a realistic data expansion method that is an actual variant of human handwriting. This is done by changing or adding more value to the parameters. Our model had just 10–11% training samples per character compared to other character-recognition methods. We achieved close to existing state-of-the-art character recognition results of all four datasets. Images reconstruction strategy was also used. Our system can be used when training data is less, and the user wants to achieve reasonable accuracy.

Keywords

Capsule networks Character-recognition CNN EMINST Deep learning 

References

  1. 1.
    Bertinetto, L., Henriques, J.F., Valmadre, J., Torr, P.H., Vedaldi, A.: Learning feed-forward one-shot learners. NIPS (2016)Google Scholar
  2. 2.
    Cohen, G., Afshar, S., Tapson, J., van Schaik, A.: EMNIST: an extension of MNIST to handwritten letters. CoRR (2017)Google Scholar
  3. 3.
    Dufourq, E., Bassett, B.A.: Eden: evolutionary deep networks for efficient machine learning. In: PRASARobMech, Bloemfontein, South Africa, pp. 110–115 (2017)Google Scholar
  4. 4.
    Hinton, G.E., Krizhevsky, A., Wang, S.D.: Transforming auto-encoders. In: ICANN, Berlin, Heidelberg, pp. 44–51 (2011)Google Scholar
  5. 5.
    Koch, G., Zemel, R., Salakhutdinov, R.: Siamese neural networks for one-shot image recognition (2015)Google Scholar
  6. 6.
    LeCun, Y., Cortes, C., Burges, C.J.C.: The MNIST database of handwritten digits (1998)Google Scholar
  7. 7.
    Zhong, Z., Zheng, L., Kang, G., Li, S., Yang, Y.: Random erasing data augmentation (2017)Google Scholar
  8. 8.
    Polesel, A., Ramponi, G., Mathews, V.J.: Image enhancement via adaptive unsharp masking. IEEE Trans. Image Process. 9, 505–510 (2000)CrossRefGoogle Scholar
  9. 9.
    Sabour, S., Frosst, N., Hinton, G.E.: Dynamic routing between capsules. In: NIPS, Long Beach, CA, pp. 3856–3866 (2017)Google Scholar
  10. 10.
    Wan, L., Zeiler, M., Zhang, S., Cun, Y.L., Fergus, R.: Regularization of neural networks using dropconnect. In: ICML, vol. 28, pp. 1058–1066 (2013)Google Scholar
  11. 11.
    Wiyatno, R., Orchard, J.: Style memory: making a classifier network generative. CoRR (2018)Google Scholar
  12. 12.
    Ciresan, D.C., Meier, U., Schmidhuber, J.: Multi-column deep neural networks for image classification. CoRR (2012)Google Scholar
  13. 13.
    Labusch, K., Barth, E., Martinetz, T.: Simple method for high-performance digit recognition based on sparse coding. IEEE Trans. Neural Netw. 19, 1985–1989 (2008)CrossRefGoogle Scholar
  14. 14.
    Ranzato, M., Poultney, C., Chopra, S., LeCun, Y.: Efficient learning of sparse representations with an energy-based model. In: Proceedings of NIPS, Cambridge, MA, pp. 1137–1144 (2006)Google Scholar
  15. 15.
    Jarrett, K., Kavukcuoglu, K., Ranzato, M., LeCun, Y.: What is the best multi-stage architecture for object recognition? In: ICCV, Kyoto, Japan, pp. 2146–2153 (2009)Google Scholar
  16. 16.
    Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Computer EngineeringFr. C. Rodrigues Institute of TechnologyVashi, Navi MumbaiIndia

Personalised recommendations