Abstract
This paper presents an approach to evaluate the quality of handwritten letters based on the set of features that are used by human handwriting experts. The use of these attributes allows very intuitive interpretation of the results and as a consequence provides solid foundation for feedback to the end user of the system. A combination of an artificial neural network and an expert system is used to evaluate and grade each handwritten letter, as well as to provide feedback to the student. The application of such a system would be in the educational field for handwriting teaching and repair.
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
Preview
Unable to display preview. Download preview PDF.
References
F. Kimura, M. Shridhar, N. Narasimhamurthi, “Lexicon Directed Segmentation-recognition Procedure for Unconstrained Handwritten Words,” Proceedings of the Thirds Int’l workshop on Frontiers Handwriting Recognition, SUNY Buffalo, May 25–27, 1993, pp.122–131.
F. Kimura, S. Tsuruoka, M. Shridhar, and Z. Chen, “Context Directed Handwritten Word Recognition for Postal Service Applications,” Proc. of Fifth Advanced Technology Conference, Wash. DC, 1992, pp. 199–213.
F. Kimura, M. Shridhar, “Segmentation-Recognition Algorithm for ZIP Code Field Recognition,” Machine Vision and Apoplications (1992) 5:199–210.
M. Belkasim, M. Shridhar, M. Ahmadi, “Pattern Recognition Using Moment Invariants: A Comparative Study,” Pattern Recognition. Vol. 24, No. 12, pp.1117–1138, 1991.
Handwriting Research and Information, An Administrator’s Handbook, ScottForesman, 1993.
Anil K. Jain, Jianchang Mao, and K.M Mohiuddin, “Artificial Neural Networks: A Tutorial,” IEEE Computer Society, Volume 29, Number 3, March 1996.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kulesh, V., Schaffer, K., Sethi, I., Schwartz, M. (2001). Handwriting Quality Evaluation. In: Singh, S., Murshed, N., Kropatsch, W. (eds) Advances in Pattern Recognition — ICAPR 2001. ICAPR 2001. Lecture Notes in Computer Science, vol 2013. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44732-6_16
Download citation
DOI: https://doi.org/10.1007/3-540-44732-6_16
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-41767-5
Online ISBN: 978-3-540-44732-0
eBook Packages: Springer Book Archive