Abstract
Fractal theory has been used for computer graphics, image compression and different fields of pattern recognition. In this paper, a fractal based method for recognition of both on-line and off-line Farsi/ Arabic handwritten digits is proposed. Our main goal is to verify whether fractal theory is able to capture discriminatory information from digits for pattern recognition task. Digit classification problem (on-line and off-line) deals with patterns which do not have complex structure. So, a general purpose fractal coder, introduced for image compression, is simplified to be utilized for this application. In order to do that, during the coding process, contrast and luminosity information of each point in the input pattern are ignored. Therefore, this approach can deal with on-line data and binary images of handwritten Farsi digits. In fact, our system represents the shape of the input pattern by searching for a set of geometrical relationship between parts of it. Some fractal-based features are directly extracted by the fractal coder. We show that the resulting features have invariant properties which can be used for object recognition.
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Mozaffari, S., Faez, K., Märgner, V. (2007). Application of Fractal Theory for On-Line and Off-Line Farsi Digit Recognition. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2007. Lecture Notes in Computer Science(), vol 4571. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73499-4_65
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DOI: https://doi.org/10.1007/978-3-540-73499-4_65
Publisher Name: Springer, Berlin, Heidelberg
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