Skip to main content
SpringerLink
Log in

Recognizing Handwritten Characters with Local Descriptors and Bags of Visual Words

  • Conference paper
  • First Online:
Engineering Applications of Neural Networks (EANN 2015)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 517))

Abstract

In this paper we propose the use of several feature extraction methods, which have been shown before to perform well for object recognition, for recognizing handwritten characters. These methods are the histogram of oriented gradients (HOG), a bag of visual words using pixel intensity information (BOW), and a bag of visual words using extracted HOG features (HOG-BOW). These feature extraction algorithms are compared to other well-known techniques: principal component analysis, the discrete cosine transform, and the direct use of pixel intensities. The extracted features are given to three different types of support vector machines for classification, namely a linear SVM, an SVM with the RBF kernel, and a linear SVM using L2-regularization. We have evaluated the six different feature descriptors and three SVM classifiers on three different handwritten character datasets: Bangla, Odia and MNIST. The results show that the HOG-BOW, BOW and HOG method significantly outperform the other methods. The HOG-BOW method performs best with the L2-regularized SVM and obtains very high recognition accuracies on all three datasets.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Abdullah, A., Veltkamp, R., Wiering, M.: Ensembles of novel visual keywords descriptors for image categorization. In: 2010 11th International Conference on Control Automation Robotics Vision (ICARCV), pp. 1206–1211, December 2010

    Google Scholar 

  2. Arróspide, J., Salgado, L., Camplani, M.: Image-based on-road vehicle detection using cost-effective histograms of oriented gradients. Visual Communication and Image Representation 24(7), 1182–1190 (2013)

    Article  Google Scholar 

  3. Bhowmik, T.K., Ghanty, P., Roy, A., Parui, S.: SVM-based hierarchical architectures for handwritten Bangla character recognition. Document Analysis and Recognition (IJDAR) 12(2), 97–108 (2009)

    Article  Google Scholar 

  4. Cireşan, D.C., Meier, U., Schmidhuber, J.: Multi-column deep neural networks for image classification. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3642–3649, June 2012

    Google Scholar 

  5. Coates, A., Carpenter, B., Case, C., Satheesh, S., Suresh, B., Wang, T., Wu, D., Ng, A.: Text detection and character recognition in scene images with unsupervised feature learning. In: 2011 International Conference on Document Analysis and Recognition (ICDAR), pp. 440–445, September 2011

    Google Scholar 

  6. Coates, A., Lee, H., Ng, A.Y.: An analysis of single-layer networks in unsupervised feature learning. In: 2011 International Conference on Artificial Intelligence and Statistics (AISTATS), pp. 215–223, April 2011

    Google Scholar 

  7. Csurka, G., Dance, C.R., Fan, L., Willamowski, J., Bray, C.: Visual categorization with bags of keypoints. In: 2004 8th European Conference on Computer Vision (ECCV), pp. 1–22 (2004)

    Google Scholar 

  8. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 The IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pp. 886–893, vol. 1, June 2005

    Google Scholar 

  9. Deepu, V., Madhvanath, S., Ramakrishnan, A.: Principal component analysis for online handwritten character recognition. In: 2004 The 17th International Conference on Pattern Recognition (ICPR), vol. 2, pp. 327–330, August 2004

    Google Scholar 

  10. Fan, R.-E., Chang, K.-W., Hsieh, C.-J., Wang, X.-R., Lin, C.-J.: LIBLINEAR: A library for large linear classification. Machine Learning Research 9, 1871–1874 (2008)

    MATH  Google Scholar 

  11. Hinton, G.E., Osindero, S., Teh, Y.-W.: A fast learning algorithm for deep belief nets. Neural Computation 18(7), 1527–1554 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  12. Hinton, G.E., Zemel, R.S.: Autoencoders, minimum description length and Helmholtz free energy. In: Cowan, J., Tesauro, G., Alspector, J. (eds.) Advances in Neural Information Processing Systems, vol. 6, pp. 3–10. Morgan-Kaufmann (1994)

    Google Scholar 

  13. Hossain, M., Amin, M., Yan, H.: Rapid feature extraction for bangla handwritten digit recognition. In: 2011 International Conference on Machine Learning and Cybernetics (ICMLC), vol. 4, pp. 1832–1837, July 2011

    Google Scholar 

  14. Karaaba, M.F., Schomaker, L., Wiering, M.: Machine learning for multi-view eye-pair detection. Engineering Applications of Artificial Intelligence 33, 69–79 (2014)

    Article  Google Scholar 

  15. Lawgali, A., Bouridane, A., Angelova, M., Ghassemlooy, Z.: Handwritten Arabic character recognition: Which feature extraction method? Advanced Science and Technology 34, 1–8 (2011)

    Google Scholar 

  16. LeCun, Y., Cortes, C.: The MNIST database of handwritten digits (1998)

    Google Scholar 

  17. Meier, U., Ciresan, D., Gambardella, L., Schmidhuber, J.: Better digit recognition with a committee of simple neural nets. In: 2011 International Conference on Document Analysis and Recognition (ICDAR), pp. 1250–1254, September 2011

    Google Scholar 

  18. Mishra, T., Majhi, B., Panda, S.: A comparative analysis of image transformations for handwritten odia numeral recognition. In: 2013 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 790–793, August 2013

    Google Scholar 

  19. Otsu, N.: A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man and Cybernetics 9(1), 62–66 (1979)

    Article  Google Scholar 

  20. Schmidhuber, J.: Deep learning in neural networks: An overview. Neural Networks 61, 85–117 (2015)

    Article  Google Scholar 

  21. Surinta, O., Schomaker, L., Wiering, M.: A comparison of feature and pixel-based methods for recognizing handwritten bangla digits. In: 2013 International Conference on Document Analysis and Recognition (ICDAR), pp. 165–169, August 2013

    Google Scholar 

  22. Takahashi, K., Takahashi, S., Cui, Y., Hashimoto, M.: Remarks on computational facial expression recognition from HOG features using quaternion multi-layer neural network. In: Mladenov, V., Jayne, C., Iliadis, L. (eds.) EANN 2014. CCIS, vol. 459, pp. 15–24. Springer, Heidelberg (2014)

    Google Scholar 

  23. Vapnik, V.N.: Statistical Learning Theory. Wiley, September 1998

    Google Scholar 

  24. Ye, P., Kumar, J., Kang, L., Doermann, D.: Unsupervised feature learning framework for no-reference image quality assessment. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1098–1105, June 2012

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Artificial Intelligence and Cognitive Engineering (ALICE), University of Groningen, Nijenborgh 9, Groningen, The Netherlands

    Olarik Surinta, Mahir F. Karaaba, Lambert R. B. Schomaker & Marco A. Wiering

  2. Department of Computer Science and Engineering, National Institute of Technology, Rourkela, Odisha, India

    Tusar K. Mishra

Authors
  1. Olarik Surinta
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mahir F. Karaaba
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tusar K. Mishra
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lambert R. B. Schomaker
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Marco A. Wiering
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Olarik Surinta .

Editor information

Editors and Affiliations

  1. Democritus University of Thrace, Orestiada, Greece

    Lazaros Iliadis

  2. Coventry University, Coventry, United Kingdom

    Chrisina Jayne

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Surinta, O., Karaaba, M.F., Mishra, T.K., Schomaker, L.R.B., Wiering, M.A. (2015). Recognizing Handwritten Characters with Local Descriptors and Bags of Visual Words. In: Iliadis, L., Jayne, C. (eds) Engineering Applications of Neural Networks. EANN 2015. Communications in Computer and Information Science, vol 517. Springer, Cham. https://doi.org/10.1007/978-3-319-23983-5_24

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-23983-5_24

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23981-1

  • Online ISBN: 978-3-319-23983-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation