Character recognition using correlation & hamming distance

In this paper, character recognition is carried out using template matching scheme using correlation. In our approach, in addition to correlation, the hamming distance was applied in the scenarios where the correlation fails. In this approach, text image is segmented into lines and then characters by basic pre-processing techniques. After that, the character image is classified and converted into text by template matching scheme using either correlation or hamming distance. For test stage, four text images were created with total of 560 characters. The system works successfully with average recognition rates of 72.39% and 94.90% for correlation only and correlation plus hamming distance, respectively.