Recognition of middle age Persian characters using a set of invariant moments

In this paper, recognition of ancient middle Persian documents is studied. Our major attention has been focused on feature extraction and classification. A set of invariant moments has been selected as the features and the minimum mean distance (three versions of which that is called MMD1, MMD2, MMD3), KNN and Parzen as the classifier. Preprocessing is also considered in this paper which allows, the effects of under sampling (resolution pyramids), smoothing, and thinning be investigated. The algorithm has been tested not only on the original and smoothed images but also on the skeletonized and under sampled version of the text under test. The results show an acceptable recognition rate with the selected features with the proposed processing for the middle age Persian. The best-achieved classification rates are 95% and 90.5% for smoothed and original character images respectively. It was interesting to note that KNN and MMD2 classifiers yielded better recognition rate.