Recognition of isolated handwritten Farsi/Arabic alphanumeric using fractal codes

Abstract

We propose a new method for isolated handwritten Farsi/Arabic characters and numerals recognition using fractal codes. Fractal codes represent affine transformations which, when iteratively applied to the range-domain pairs in an arbitrary initial image, give results 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, which are used as inputs for a multilayer perceptron neural network for learning and identifying an input. This method is robust to scale and frame size changes. Farsi's 32 characters are categorized to 8 different classes in which the characters are very similar to each other. There are ten digits in the Farsi/Arabic languages, but since two of them are not used in postal codes in Iran, only 8 more classes are needed for digits. According to experimental results, classification rates of 91.37% and 87.26% were obtained for digits and characters respectively on the test sets gathered from various people with different educational background and different ages.