Recognition of Handwritten Persian Two-digit Numerals Using a Novel Hybrid SVM/HMM algorithm

Abstract

There is a vast range of potential applications for recognition of handwritten Persian / Arabic digits (e.g. banking transactions, business registration forms and postal code recognition to name a few). In this paper, a new method is presented for automatic recognition of joint two-digit Persian numerals. The proposed method is composed of a combinational structure of Support Vector Machines (SVM) and a Hidden Markov Models (HMM). In this approach, we used SVM and HMM for classification and segmentation goals respectively. Due to the higher performance of SVM in classification with respect to HMM, the main core of recognition is an SVM classifier. In contrast, we used HMM to detect the location of the boundary for two-digit numerals. To evaluate the method, we employed a selection of HADAF Persian isolated characters corpus. We employed a 4 scale Gabor filter bank (24, 12, 6 and 3 scales) in 6 directions (0, 30, 60, 90, 120, 150 degrees) for feature extraction. The results showed the digit recognition rate of about 98.75 percent for the proposed algorithm on Persian two-digit numerals, while the recognition rates were 98.58 and 95.93 for separate SVM and HMM engines on isolated characters respectively.