Abstract
This paper presents a character segmentation algorithm for unconstrained cursive handwritten text. The transformation-based learning method and a simplified variation of it are used in order to extract automatically rules that detect the segment boundaries. Comparative experimental results are given for a collection of multi-writer handwritten words. The achieved accuracy in detecting segment boundaries exceeds 82%. Moreover, limited training data can provide very satisfactory results.