This paper deals with the use of Bayesian Belief Networks in order to improve the accuracy and training time of character segmentation for unconstrained handwritten text. Comparative experimental results have been evaluated against Naive Bayes classification, which is based on the assumption of the independence of the parameters and two additional previous commonly used methods. Results have depicted that obtaining the inferential dependencies of the training data, could lead to the reduction of the required training time and size by a factor of 55%. Moreover, the achieved accuracy in detecting segment boundaries exceeds 86% whereas limited training data are proved to endow with very satisfactory results.
['Manolis Maragoudakis', 'Ergina Kavallieratou', 'Nikos Fakotakis', 'George K. Kokkinakis']
How conditional independence assumption affects handwritten character segmentation