A Fused Feature Extraction Approach to OCR: MLP vs. RBF

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 248)


This paper is focused on evaluating the capability of MLP and RBF neural network classifier algorithms for performing handwritten character recognition task. Projection profile features for the character images are extracted and merged with the binarization features obtained after preprocessing every character image. The fused features thus obtained are used to train both the classifiers i.e. MLP and RBF Neural Networks. Simulation studies are examined extensively and the proposed fused features are found to deliver better recognition accuracy when used with RBF Network as a classifier.


MLP RBF Hybrid Feature Extraction Character Recognition OCR Neural Network 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Alginahi, Y.: Preprocessing Techniques in Character Recognition. In: Mori, M. (ed.) Character Recognition, pp. 1–20. InTechopen Publishers (2010) ISBN: 978-953-307-105-3Google Scholar
  2. 2.
    Blumenstein, M., Liu, X.Y., Verma, B.: An investigation of the modified direction feature for cursive character recognition. Pattern Recognition 40, 376–388 (2007)CrossRefMATHGoogle Scholar
  3. 3.
    Wang, X., Ding, X., Liu, C.: Gabor filters-based feature extraction for character recognition. Pattern Recognition 38(3), 369–379 (2005)MathSciNetCrossRefMATHGoogle Scholar
  4. 4.
    Cavalin, P.R., Britto, A.S., Bortolozzi, F., Sabourin, R., Oliveira, L.S.: An implicit segmentation based method for recognition of handwritten strings of characters. In: Proceedings of ACM Symposium on Applied Computing, pp. 836–840 (2006)Google Scholar
  5. 5.
    Blumenstein, M., Verma, B., Basli, H.: A Novel Feature Extraction Technique for the Recognition of Segmented Handwritten Characters. In: Proceedings of the 7th International Conference on Document Analysis and Recognition, pp. 137–141. IEEE Computer Society Press, Edinburgh (2003)Google Scholar
  6. 6.
    Blumenstein, M., Verma, B.: Analysis of Segmentation Performance on the CEDAR Benchmark Database. In: Proceedings of the 6th International Conference on Document Analysis and Recognition, pp. 1142–1146. IEEE Computer Society Press, Seattle (2001)Google Scholar
  7. 7.
    Choudhary, A., Rishi, R., Ahlawat, S.: A New Character Segmentation Approach for Off-Line Cursive Handwritten Words. Elsevier Procedia Computer Science 17, 434–440 (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Maharaja Surajmal InstituteNew DelhiIndia
  2. 2.UIET, Maharshi Dayanand UniversityRohtakIndia

Personalised recommendations