An Effective Stochastic Estimation of Handwritten Character Segmentation Bounds

Abstract-- This paper studies 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 other 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

How Conditional Independence Assumption affects Handwritten Character Segmentation

Abstract. 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. 1

Abstract The GRUHD Database of Greek Unconstrained Handwriting

In this paper we present the GRUHD database of Greek characters, text, digits, and other symbols in unconstrained handwriting mode. The database consists of 1,760 forms that contain 667,583 handwritten symbols and 102,692 words in total, written by 1,000 writers, 500 men and equal number of women. Special attention was paid in gathering data from writers of different age and educational level. The GRUHD database is accompanied by the GRUHD software that facilitates its installation and use and enables the user to extract and process the data from the forms selectively, depending on the application. The various types of possible installations make it appropriate for the training and validation of character recognition, character segmentation and text-dependent writer identification systems.