Abstract
In the paper the multilevel probabilistic approach to handprinted form recognition is described. The form recognition is decomposed into three levels: character recognition, word recognition and form contents recognition. On the word and form contents level the probabilistic lexicons are available. The decision on the word level is performed using probabilistic properties of character classifier and the contents of probabilistic lexicon. The novel approach to combining these two sources of information about classes (words) probabilities is proposed, which is based on lexicons and accuracy assessment of local character classifiers. Some experimental results and examples of practical applications of recognition method are also briefly described.
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
Kuncheva L.I. (2002) A Theoretical Study on Six Classifier Fusion Strategies, IEEE Trans. on Pattern Anal. and Machine Intelligence, Vol. 24, No 2: 281–286
Chen W.T., Gader P., Shi H. (1999) Lexicon-Driven Handwritten Word Recognition Using Optimal Linear Combination of Order Statistics, IEEE Trans. on Pattern Anal. and Machine Intelligence, Vol 21, No 1: 71–82
Grandidier F., Sabourin R. (2000) A New Strategy for Improving Features Set in a Discrete HMM-based Handwriting Recognition System, In: Schomaker L.R.B., Vuurpijl (eds) Proceedings of the Seventh International Workshop on Frontiers in Handwritting Recognition, Sept. 11–13 2000 Amsterdam: 113–122
Kim J. H., Kim K.K., Suen C. Y. (2000) An HMM-MLP Hybrid Model for Cursive Script Recognition. Pattern Analysis and Applications, No 3: 312–324
Kuncheva L.I. (2001) Using Measures of Similarity and Inclusion for Multiple Classifier Fusion by Decision Templates, Fuzzy Sets and Systems, 122(3): 401–407
Liu C., Nakashima K., Sako H., Fujisawa H. (2003) Handwritten Digit Recognition: Benchmarking of State-of-the-Art Techniques, Pattern Recognition, Vol. 36: 2271–2285
Lu Y., Gader P., Tan C. L. (2002) Combination of Multiple Classifiers Using Probabilistic Dictionary and its Application to Postcode Generation, Pattern Recognition, Vol 35: 2823–2832
Sas J. (2004) Handwritten Laboratory Test Order Form Recognition Module For Distributed Clinic, Journ. of Medical Informatics & Technologies, Vol 8: 59–68
Sas J. (2004) Three-Level, Lexicon-Based Handwritten Form Recognition Method, In: Klopotek M., Tchorzewski J. (eds) Proc, of VI Int. Conf on Artificial Intelligence AI-19’2004, Vol. 1(23): 113–124
Vinciarelli A., Bengio S., Bunke H. (2004) Offline Recognition of Unconstrained Handwritten Texts Using HMMs and Statistical Language Models, IEEE Trans. on Pattern Anal. and Machine Intelligence, Vol 26, No 6: 709–720
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sas, J., Kurzynski, M. (2005). Multilevel Recognition of Structured Handprinted Documents - Probabilistic Approach. In: Kurzyński, M., Puchała, E., Woźniak, M., żołnierek, A. (eds) Computer Recognition Systems. Advances in Soft Computing, vol 30. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-32390-2_85
Download citation
DOI: https://doi.org/10.1007/3-540-32390-2_85
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25054-8
Online ISBN: 978-3-540-32390-7
eBook Packages: EngineeringEngineering (R0)