Font recognition for persian optical character recognition system

Font recognition is one of the pre-processing steps in optical character recognition (OCR) systems that affects on their performance. In this paper two methods are proposed for Persian font recognition. In the first method, Gabor filter is used for feature extraction from the images, then principle component analysis (PCA) applied to reduce feature dimensions and finally, a multi-layer Perceptron (MLP) neural network is used for the classification. In the second techniques, random forest is utilized for recognizing fonts. For evaluation, a dataset includes 10 popular Persian fonts is used. The proposed Gabor-PCA-MLP method has achieved 98.70% of F-measure, and random forest resulted in of 96.95% of F-measure.