Fusion of multiple handwritten word recognition techniques

Fusion of multiple handwritten word recognition techniques is described. A novel Borda count for fusion based on ranks and confidence values is propos ed. Three techniques with two different conventional segmentation algorithm s in conjunction with backpropagation and radial basis function neural netw orks have been used in this research. Development has taken place at the Un iversity of Missouri and Griffith University. All experiments were performe d on real-world handwritten words taken from the CEDAR benchmark database. The word recognition results are very promising and the highest (91%) among published results for handwritten words. (C) 2001 Published by Elsevier Sc ience B.V.