Handwritten character recognition by Fourier descriptors and neural network

This paper describes a handwritten character recognition system by using a multi-layer perceptron with one hidden layer. Features extracted from the handwritten characters are Fourier descriptor (FD) and border transition technique (BTT). The FDs and border transition values are input to the neural network which is then trained by backpropagation. Test results indicate that FD combined with BTT can provide good recognition accuracy (96%) for handwritten numerals 0 to 9.