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| Saeed Mozaffari, Karim Faez, Karim Faez, Majid Ziaratban, "Structural Decomposition and Statistical Description of Farsi/Arabic Handwritten Numeric Characters," Document Analysis and Recognition, International Conference on, pp. 237-241, Eighth International Conference on Document Analysis and Recognition (ICDAR'05), 2005. | |||
| BibTex | x | ||
| @article{ 10.1109/ICDAR.2005.221, author = {Saeed Mozaffari and Karim Faez and Karim Faez and Majid Ziaratban}, title = {Structural Decomposition and Statistical Description of Farsi/Arabic Handwritten Numeric Characters}, journal ={Document Analysis and Recognition, International Conference on}, volume = {0}, year = {2005}, issn = {1520-5263}, pages = {237-241}, doi = {http://doi.ieeecomputersociety.org/10.1109/ICDAR.2005.221}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - CONF JO - Document Analysis and Recognition, International Conference on TI - Structural Decomposition and Statistical Description of Farsi/Arabic Handwritten Numeric Characters SN - 1520-5263 SP237 EP241 A1 - Saeed Mozaffari, A1 - Karim Faez, A1 - Karim Faez, A1 - Majid Ziaratban, PY - 2005 KW - null VL - 0 JA - Document Analysis and Recognition, International Conference on ER - | |||
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.