Handwritten Thai Character Recognition Using Fourier Descriptors and Genetic Neural Networks

Abstract

This article presents a method to solve the rotated and scaling character recognition problem using Fourier descriptors and genetic neural networks. The contours of character image are extracted and separated between the outer contour and inner or loop contours. The loop contours are a special characteristic of Thai characters, called the head of the character. The special features of Thai characters (loop contours) are used at the rough classification stage, and Fourier descriptors with genetic neural networks are used at the fine classification stage. The Fourier descriptors detect the outer contour of a character and it is fed to network. These features are recognized by a multilayer neural network. Genetic algorithms (GAs) are utilized to help compute the weights of the neural network optimally and reduce uncertain states in the neural networks output. Experimental results have shown that the combination of the Fourier descriptors with genetic neural networks, loop features, and local curvature charateristics of similar characters are powerful tools for successfully classifying Thai characters. The recognition rate by this method is 99.12% for 1200 examples of handwritten Thai words (a total of 13,500 characters) written by 60 persons.