Structural Decomposition and Statistical Description of Farsi/Arabic Handwritten Numeric Characters
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Character Representation and Recognition Using Quadtree-based Fractal Encoding Scheme
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Application of Fractal Theory for On-Line and Off-Line Farsi Digit Recognition
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
Accurate tool based on JPEG image compression for Arabic handwritten character shape recognition
AIA '08 Proceedings of the 26th IASTED International Conference on Artificial Intelligence and Applications
Real time fractal image coder based on characteristic vector matching
Image and Vision Computing
Recognition of Arabic (Indian) bank check digits using log-gabor filters
Applied Intelligence
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In this paper we proposed a new method for isolated handwritten Farsi/Arabic characters and numerals recognition using fractal codes and Haar wavelet transform. Fractal codes represent affine transformations which when iteratively applied to the range-domain pairs in an arbitrary initial image, the result is close to the given image. Each fractal code consists of six parameters such as corresponding domain coordinates for each range block, brightness offset and an affine transformation. in this system, The support vector machine (SVM) whih is based on statistical learning theory, with good generalization ability is used as the classifier. This method is robust to scale and frame size changes. 32 Farsi's characters are categorized to 8 different classes in which the characters are very similar to each others. There are ten digits in Farsi/Arabic language and since two of them are not used in the postal codes in Iran, therefore 8 more classes are needed for digits. According to experimental results, classification rates of 92.71% and 92% were obtained for digits and characters respectively on the test sets gathered from various people with different educational background and different ages.