A new type of recurrent neural network for handwritten character recognition
Seong–Whan Lee, Young‐Joon Kim · 2002
The authors propose a new type of recurrent neural network for handwritten character recognition. The proposed recurrent neural network differs from Jordan and Elman recurrent neural networks in view of functions and architectures because it was originally extended from the multilayer feedforward neural network for improving discrimination and generalization power in recognizing handwritten characters. They also analyze the performance of the proposed recurrent neural network by performing recognition experiments with the totally unconstrained handwritten numeral database of Concordia University of Canada. The experimental results showed that the proposed recurrent neural network greatly improves the discrimination and generalization power.