Fractal image compression: theory and application
Fractal image compression: theory and application
Recognition of Handwritten Cursive Arabic Characters
IEEE Transactions on Pattern Analysis and Machine Intelligence
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 5 - Volume 5
Writer Identification based on the fractal construction of a reference base
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Scale and rotational invariant object recognition using fractal transformations
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 06
Face recognition by fractal transformations
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 06
Feature generation from digital images using pseudo-fractal algorithm and its four modifications
Machine Graphics & Vision International Journal
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
Handwritten character recognition through two-stage foreground sub-sampling
Pattern Recognition
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In this paper we proposed a new method for isolated handwritten Farsi/Arabic numerals recognition using quadtree-based fractal representation and iterated function system. Fractal codes represent affine transformations which when iteratively applied to the rangedomain pairs in an arbitrary initial image, the result is close to the given image. Each fractal code consists of six parameters such as the corresponding domain coordinates for each range block, brightness offset and an affine transformation. Based on fixed point theorem in iterated function system, we introduced fractal transformation classifier for optical character recognition. We also used Euclidean distance between fractal codes of a query image and fractal codes of all images in the database as a measure of distance for classification. Since fractal codes have different lengths, we applied PCA algorithm to normalize their lengths. There are ten digits in Farsi/Arabic language and since two of them are not used in Iran postal codes, therefore 8 classes are needed for digits. By using fractal codes with nearest neighbor classifier and fractal transformation, the recognition rate of 92.6% is obtained on our numeral database which contains 480 samples per digit and was gathered from more than 200 people with different ages and different educational background.