Fractal and Multi-Fractal Dimensions for Farsi/Arabic Font Type and Size Recognition

In this paper, a new method based on fractal geometry is proposed for Farsi/Arabic font recognition. The feature extraction does not depend on the document contents which considers font recognition problem as texture identification task The main features are obtained by combining the BCD, DCD, and DLA techniques. Dataset includes 2000 samples of 10 typefaces, each containing four sizes. The average recognition rates obtained for these 10 fonts and 4 sizes (40 classes) using RBF and KNN classifiers are 96% and 91% respectively. The dimension of feature vectors extracted by the proposed fractal approach is very low. This property obviates the need for numerous training samples. Experimental results show that this algorithm is robust against skew. Simultaneously identifying type and size of the font is the most important innovation of this paper.