Feature extraction with wavelet transform for recognition of isolated handwritten Farsi/Arabic characters and numerals

Abstract

A system is developed for recognition of handwritten Farsi/Arabic characters and numerals. The discrete wavelet transform is utilized to produce wavelet coefficients, which are used for classification. We used Haar wavelet for feature extraction in this system. The extracted features are used as training inputs to a feed forward neural network using the backpropagation learning rule. The learning and test patterns were gathered from various people with different educational backgrounds and different ages. We categorize 32 characters in Farsi language to 8 different classes in which characters of each class are very similar to each others. There are ten digits in Farsi/Arabic languages, but two of them are not used in postal codes in Iran, so we have 8 different extra classes for digits. This system yields the classification rates of 92.33% and 91.81% for these 8 classes of handwritten Farsi characters and numerals respectively. We used this system for recognizing the handwritten postal addresses which contain the names of cities and their postal codes. Our database contains 579 postal addresses in Iran. The system yields a recognition rate of 97.24% for these postal addresses.