iorewpics.blogg.se

B farsi fonts
B farsi fonts










b farsi fonts

Ghorbannia Delavar, Some new mutation operators for genetic data clustering, The Journal of Mathematics and Computer Science, 12 (2014), 282-294. Mirpour, Introduce a New Algorithm for Data Clustering by Genetic Algorithm, The Journal of Mathematics and Computer Science, 10 (2014), 144 – 156. Fakhar, A Novel Document Clustering Algorithm Based on Ant Colony Optimization Algorithm, The Journal of mathematics and computer Science, 7 (2013), 171-180. Fakhar, A Novel Method for Document Clustering using Ant-Fuzzy Algorithm, The Journal of Mathematics and Computer Science, 4 (2012), 182 – 196. Heidari, Representing the New Model for Improving K-Means Clustering Algorithm based on Genetic Algorithm, The Journal of Mathematics and Computer Science, 2 (2011), 329-336. Hussain, Feature Extraction for Facial Expression Recognition based on Hybrid Face Regions, Advances in Electrical and Computer Engineering, 9 (2009), 63-67. Bagheri, Content-Independent Farsi Font Recognition Based on Dynamic Most-Frequent Connected Components, 21st International Conference on Pattern Recognition (ICPR 2012) Tsukuba, Japan, 11-15 (2012), 729-733. Bagheri, Improving Farsi font recognition accuracy by using proposed directional elliptic Gabor filters, First Iranian Conference on Pattern Recognition and Image Analysis (PRIA), (2013), 1 – 5. Kabir, Farsi font recognition based on Sobel–Roberts features, Pattern Recognition Letters, 31 (2010), 75–82. Journal of Intelligent Systems and Technologies, 2 (2007), 178-183. Hamidi, Support Vector Machine for Persian Font Recognition, Int. Benabdelhafid, Can Fractal Dimension Be Used in Font Classification, In Proc. Abuhaiba, Arabic Font Recognition Using Decision Trees Built from Common Words, Journal of Computing and Information Technology (CIT), 13 (2005), 211-223.ī. Arab Journal of Information Technology, 1 (2003), 33-39. Abuhaiba, Arabic Font Recognition Based on Templates, Int. Wu, Character Independent Font Recognition on a Single Chinese Character, IEEE Trans. Suen, An EMD-based Recognition Method for Chinese Fonts and Styles, Pattern Recognition Letters, 27 (2006), 1692-1701. Juang, Chinese text distinction and font identification by recognizing most frequently used characters, Image and Vision Computing, 19 (2001), 329-338. Emptoz, Font Type Extraction and Character Prototyping Using Gabor Filters, In Proc. Garain, Extraction of Type Style-based Meta-information from Image Documents, IJDAR, 3 (2001), 138-149.ī. Perez, High-order Statistical Texture Analysis-Font Recognition Applied, Pattern Recognition Letters, 26 (2005), 135-145.ī. Kim, Word-Level Optical Font Recognition Using Typographical Features, IJPRAI, 18 (2004), 541-561.Ĭ. 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.












B farsi fonts