Fusion of Multiple Handwritten Word Recognition Techniques

Abstract

Fusion of multiple handwritten word recognition techniques is described. A novel borda count for fusion based on ranks and confidence values is proposed. Three techniques with two different conventional segmentation algorithms in conjunction with backpropagation and radial basis function neural networks have been used in this research. Development has taken place at the University of Missouri and Griffith University. All experiments were performed on real-world handwritten words taken from the CEDAR benchmark database. The word recognition results are very promising and highest (91 96) among published results for handwritten words