Handwritten Character Segmentation Using Transformation-Based Learning

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 multiwriter handwritten words. The achieved accuracy in detecting segment boundaries exceeds 82%. Moreover, limited training data can provide very satisfactory results.

• Type: Conference
• Year: 2000
• Where: ICPR
• Authors: Ergina Kavallieratou, Efstathios Stamatatos, Nikos Fakotakis, George K. Kokkinakis
• Keywords: Character Segmentation Algorithm, Cursive Handwritten Text, ICPR 2000, Segment Boundaries