Abstract
Most handwriting recognition systems need a mechanism for handling classification errors. These errors are typically caused by the large shape variability of the handwriting produced by different writers and by the segmentation errors, which occur when the word recognition process is performed by extracting and classifying single characters. In this paper, in order to reduce the segmentation errors, we propose a hierarchical recognition system composed of two classification modules. The first one discriminates isolated characters from cursive fragments using specifically devised features. The second one is an OCR engine that receives as input only those samples classified as isolated characters in the previous module. The whole system works like a highly reliable OCR that rejects most of the cursive fragments avoiding their incorrect classification. The experimental results confirmed the effectiveness of the proposed system.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
Chow, C.: On optimum recognition error and reject trade of. IEEE Trans. Inf. Theor. 16(1), 41–46 (2006)
Cordella, L.P., De Stefano, C., Fontanella, F., Scotto di Freca, A.: A weighted majority vote strategy using bayesian networks. In: Petrosino, A. (ed.) ICIAP 2013, Part II. LNCS, vol. 8157, pp. 219–228. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-41184-7_23
De Stefano, C., Fontanella, F., Marcelli, A., Parziale, A., Scotto di Freca, A.: Rejecting both segmentation and classification errors in handwritten form processing. In: Proceedings of International Conference on Frontiers in Handwriting Recognition, ICFHR, pp. 569–574, September 2014
De Stefano, C., Fontanella, F., Marrocco, C., Scotta di Freca, A.: A hybrid evolutionary algorithm for bayesian networks learning: an application to classifier combination. In: Di Chio, C., Cagnoni, S., Cotta, C., Ebner, M., Ekárt, A., Esparcia-Alcazar, A.I., Goh, C.-K., Merelo, J.J., Neri, F., Preuß, M., Togelius, J., Yannakakis, G.N. (eds.) EvoApplications 2010, Part I. LNCS, vol. 6024, pp. 221–230. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-12239-2_23
De Stefano, C., Fontanella, F., Scotto di Freca, A.: A novel naive bayes voting strategy for combining classifiers. In: Proceedings of International Workshop on Frontiers in Handwriting Recognition, IWFHR, pp. 467–472 (2012)
De Stefano, C., Sansone, C., Vento, M.: To reject or not to reject: that is the question-an answer in case of neural classifiers. IEEE Trans. Syst. Man Cyber. Part C 30(1), 84–94 (2000)
De Stefano, C., Fontanella, F., Maniaci, M., Scotto di Freca, A.: A method for scribe distinction in medieval manuscripts using page layout features. In: Maino, G., Foresti, G.L. (eds.) ICIAP 2011, Part I. LNCS, vol. 6978, pp. 393–402. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-24085-0_41
Fumera, G., Roli, F., Giacinto, G.: Reject option with multiple thresholds. Pattern Recognit. 33, 2099–2101 (2000)
Hall, M.: Correlation-based feature selection for machine learning. Ph.D. thesis. University of Waikato (1999)
Hanczar, B., Dougherty, E.R.: Classification with reject option in gene expression data. Bioinformatics 24(17), 1889–1895 (2008)
Koerich, A.: Rejection strategies for handwritten word recognitions. In: IWFHR-9, pp. 479–484. IEEE Computer Society Press (2004)
Kononenko, I.: Estimating attributes: Analysis and extensions of RELIEF. In: Bergadano, F., De Raedt, L. (eds.) ECML 1994. LNCS, vol. 784, pp. 171–182. Springer, Heidelberg (1994). https://doi.org/10.1007/3-540-57868-4_57
Oliveira, L.S., Sabourin, R., Bortolozzi, F., Suen, C.: Automatic recognition of handwritten numerical strings: a recognition and verification strategy. IEEE Trans. Pattern Anal. Mach. Intell. 24(11), 1438–1454 (2002)
Landgrebe, T., Tax, D.M.J., Paclk, P., Duin, R.P.W.: The interaction between classification and reject performance for distance-based reject-option classifiers. Pattern Recognit. Lett. 27(8), 908–917 (2006)
Liu, H., Setiono, R.: Chi2: feature selection and discretization of numeric attributes. In: ICTAI, pp. 88–91. IEEE Computer Society (1995)
Plamondon, R., Srihari, S.: On-line and o-line handwriting recognition: a comprehensive survey. IEEE Trans. Pattern Anal. Mach. Intell. 22(1), 63–84 (2000)
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Series in Machine Learning. Morgan Kaufmann, Los Altos (1993)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
De Stefano, C., Fontanella, F., Marcelli, A., Parziale, A., Scotto di Freca, A. (2018). Recovering Segmentation Errors in Handwriting Recognition Systems. In: Huang, DS., Jo, KH., Zhang, XL. (eds) Intelligent Computing Theories and Application. ICIC 2018. Lecture Notes in Computer Science(), vol 10955. Springer, Cham. https://doi.org/10.1007/978-3-319-95933-7_72
Download citation
DOI: https://doi.org/10.1007/978-3-319-95933-7_72
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-95932-0
Online ISBN: 978-3-319-95933-7
eBook Packages: Computer ScienceComputer Science (R0)