Wang, Font Recognition Based on Global Texture Analysis, IEEE Trans. Srihari, Multifont Classification using Typographical Attributes, In Proc. Ingold, Optical Font Recognition Using Typographical Features, IEEE Trans. Ingold, Optical Font Recognition from Projection Profiles, Electronic Publishing, 6 (1993), 249-260.Ī. Doermann, Font Identification Using the Grating Cell Texture Operator, In Proc. Nagy, A Self-Correcting 100-Font Classifier, In Proc. 'Farsi Font Recognition Based on the Fonts of Text Samples Extracted by Som.' Journal of Mathematics and Computer Science, 15, no. Experiments show that the proposed method outperforms The size of a particular MFCC in a text image. The font size estimation is carried out based on Procedure reduces the complexities and processing time. The second phase after the phase of the font type and style recognition. To achieve a more accurate algorithm with lower complexity, the font size is determined in The most frequent recognized font of the extracted samples is considered as the font of Type and font style of the extracted test samples are recognized by matching between them and the Samples of the detected MFCCs for a test image surely are in the extracted training samples set. Since theįrequent samples in different Farsi texts are very similar, it can be guaranteed that a large number of This procedure is applied to both training and test images. A number of members of these big clusters areĮxtracted from the input image. The most frequent connected components (MFCCs). The feature vectors are clusteredīy using a Self-Organizing Map (SOM) clustering method. Someįeatures are extracted from the connected components of a text image. Majid Ziaratban - Engineering Faculty, Golestan University, Gorgan, Iran Fatemeh Bagheri - Engineering Faculty, Golestan University, Gorgan, IranĪ Farsi font recognition algorithm based on the fonts of some frequent text samples is proposed.

Farsi fonts for word