Handwritten Recognition Using an Ensemble Technique based on the Firefly Algorithm

Handwritten digit recognition, as an important issue in pattern classification, has received considerable attention of many researchers to develop theoretical and practical aspects of this problem. The goal is to recognize a printed digit text or a scanned handwritten using an automated procedure. In this paper an optical character recognition system with a multi-step procedure is presented for Farsi handwritten digit classification. First, a pre-processing course is performed on the image to enhance and make prepare the image for the main steps. Then multiple features are extracted which are believed to be effective in the classification step, among which a multi-objective genetic algorithm selects those with more discriminative characteristics in order to reduce the computational complexity of the classification steps. Following this, only the selected features are extracted from the digit images to be entered to three classifiers. Once the classifiers are trained, their outputs are fed into a classifier ensemble to make the final decision. The weights of the linear combination of classifiers are adjusted by an evolutionary firefly algorithm which optimizes the F-criterion. Evaluation of the proposed technique on the standard HODA database demonstrates that the algorithm of this paper attains higher performance indices including F-measure of 98.88%, compared to other existing methods.