A high accuracy algorithm for recognition of handwritten numerals
Pattern Recognition
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Recognition of Handwritten Cursive Arabic Characters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Structural Decomposition and description of Printed and Handwritten Characters
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume III-Volume 7276 - Volume 7276
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Information Sciences: an International Journal
Offline Arabic Handwriting Recognition: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognition of off-line printed Arabic text using Hidden Markov Models
Signal Processing
Expert Systems with Applications: An International Journal
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
Gabor features for offline Arabic handwriting recognition
DAS '10 Proceedings of the 9th IAPR International Workshop on Document Analysis Systems
Recognition of Arabic (Indian) bank check digits using log-gabor filters
Applied Intelligence
Offline arabic handwritten text recognition: A Survey
ACM Computing Surveys (CSUR)
A novel free format Persian/Arabic handwritten zip code recognition system
Computers and Electrical Engineering
Recognition of Bangla compound characters using structural decomposition
Pattern Recognition
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A Statistical method embedded with statistical features is proposed for Farsi/Arabic handwritten zip code recognition in this paper. The numeral is first smoothed and the skeleton is obtained. A set of feature points are then detected and the skeleton is decomposed into primitives. A primitive code includes the information of each primitive and a global code is derived from the primitive codes to describe the topological structure of the skeleton. By using the average and variance of X and Y changes in each primitive, the Direction and curvature of the skeleton can be statistically described. Since the global codes have different lengths, we applied PCA algorithm to normalize their lengths. Thanks to statistically description of the skeleton, we can use the nearest neighbor classifier for recognition. According to experimental results, classification rate of 94.44% is obtained for numerals on the test sets gathered from various people with different educational background and different ages. Our database includes 480 samples per digit. We used 280 samples of each digit for training and the rest (200) for test.