Feature comparison between fractal codes and wavelet transform in handwritten alphanumeric recognition using SVM classifier

Abstract

We proposed a new method for isolated handwritten Farsi/Arabic characters and numerals recognition using fractal codes and Haar wavelet transform. Fractal codes represent affine transformations which when iteratively applied to the range-domain pairs in an arbitrary initial image, the result is close to the given image. Each fractal code consists of six parameters such as corresponding domain coordinates for each range block, brightness offset and an affine transformation, in this system, The support vector machine (SVM) which is based on statistical learning theory, with good generalization ability is used as the classifier. This method is robust to scale and frame size changes. 32 Farsi 's characters are categorized to 8 different classes in which the characters are very similar to each other. There are ten digits in Farsi/Arabic language and since two of them are not used in the postal codes in Iran, therefore 8 more classes are needed for digits. According to experimental results, classification rates of 92.71% and 92% were obtained for digits and characters respectively on the test sets gathered from various people with different educational background and different ages.