Bayes Classification of Online Arabic Characters by Gibbs Modeling of Class Conditional Densities

Authors:
Neila Mezghani;Amar Mitiche;Mohamed Cheriet
Affiliations:
-;-;-
Venue:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Year:
2008

Citing 0
Cited 4

A Gibbsian Kohonen Network for Online Arabic Character Recognition

ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing, Part II
Integration of Contextual Information in Online Handwriting Representation

ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
A spatially adaptive statistical method for the binarization of historical manuscripts and degraded document images

Pattern Recognition
A classifier for Bangla handwritten numeral recognition

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.15

Visualization

Abstract

This study investigates Bayes classification of online Arabic characters represented by histograms of tangent differences and Gibbs modeling of the class-conditional probability density functions. The parameters of these Gibbs density functions are estimated following the Zhu, Wu, and Mumford constrained maximum entropy formalism, originally introduced for image and shape synthesis. We investigate two partition function estimation methods: one uses the training sample and the other draws from a reference distribution. The efficiency of the corresponding Bayes decision methods, and of a combination of these, is shown in experiments using a database of 9504 freely written samples by 22 scriptors. Comparisons to the nearest neighbor rule method and Kohonen neural network methods are provided.