Abstract
This paper presents a two-stage handwriting recognizer for classification of isolated characters that exploits explicit knowledge on characters’ shapes and execution plans. The first stage performs prototype extraction of the training data using a Fuzzy ARTMAP based method. These prototypes are able to improve the performance of the second stage consisting of LVQ codebooks by means of providing the aforementioned explicit knowledge on shapes and execution plans. The proposed recognizer has been tested on the UNIPEN international database achieving an average recognition rate of 90.15%, comparable to that reached by humans and other recognizers found in literature.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bote-Lorenzo, M. L., Dimitriadis, Y. A., Gómez-Sánchez, E.: Allograph Extraction of Isolated Handwritten Characters. Proc. of the Tenth Biennial Conference of the International Graphonomics Society, 2001. IGS’01, Nijmegen, The Netherlands (2001) 191–196
Devijver, P. A., Kittler, J.: Pattern Recognition: a Statistical Approach. Prentice-Hall International, London (1982)
Gómez-Sánchez, E., Dimitriadis, Y. A., Sánchez-Reyes Mas, M., Sánchez García, P., Cano Izquierdo, J. M., López Coronado, J.: On-Line Character Analysis and Recognition With Fuzzy Neural Networks. Intelligent Automation and Soft Computing. 7(3) (2001)
Gómez-Sánchez, E., Gago González, J. Á., Dimitriadis, Y. A., Cano Izquierdo, J. M., López Coronado, J.: Experimental Study of a Novel Neuro-Fuzzy System for on-Line Handwritten UNIPEN Digit Recognition. Pattern Recognition Letters. 19(3) (Mar. 1998) 357–364
Guyon, I., Schomaker, L., Plamondon, R., Liberman, M., Janet, S.: UNIPEN Project of on-Line Data Exchange and Recognizer Benchmarks. Proc. of the 12th International Conference on Pattern Recognition, Jerusalem, Israel (1994) 9–13
Kohonen, T.: Self-Organizing Maps. 2nd edn. Springer-Verlag, Heidelberg (1997)
Kohonen, T., Kangas, J., Laaksonen, J., Torkkola, K.: LVQ-PAK: The Learning Vector Quantization Program Package. Helsinki University of Technology, Finland (1995)
Liu, C.-L., Nakagawa, M.: Evaluation of Prototype Learning Algorithms for Nearest-Neighbor Classifier in Application to Handwritten Character Recognition. Pattern Recognition. 34 (2001) 601–615
Plamondon, R., Srihari, S. N.: On-Line and Off-Line Handwriting Recognition: a Comprehensive Survey. IEEE Trans. on Pattern Analysis and Machine Intelligence. 22(1) (Jan. 2000) 63–84
Vuurpijl, L., Schomaker, L.: Finding Structure in Diversity: a Hierarchical Clustering Method for the Categorization of Allographs in Handwriting. Proc. of the International Conference on Document Analysis and Recognition, 1997. ICDAR’97 (1997) 387–393
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Bote-Lorenzo, M.L., Dimitriadis, Y.A., Gómez-Sánchez, E. (2002). A Hybrid Two-Stage Fuzzy ARTMAP and LVQ Neuro-fuzzy System for Online Handwriting Recognition. In: Dorronsoro, J.R. (eds) Artificial Neural Networks — ICANN 2002. ICANN 2002. Lecture Notes in Computer Science, vol 2415. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46084-5_71
Download citation
DOI: https://doi.org/10.1007/3-540-46084-5_71
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-44074-1
Online ISBN: 978-3-540-46084-8
eBook Packages: Springer Book Archive