Performance Comparison between Zernike Moment Invariant and Fractal Codes features in the Application of Zip Code Recognition using RBF Neural Network
عنوان مقاله: Performance Comparison between Zernike Moment Invariant and Fractal Codes features in the Application of Zip Code Recognition using RBF Neural Network
شناسه ملی مقاله: ICS06_052
منتشر شده در ششمین کنفرانس سراسری سیستم های هوشمند در سال 1383
شناسه ملی مقاله: ICS06_052
منتشر شده در ششمین کنفرانس سراسری سیستم های هوشمند در سال 1383
مشخصات نویسندگان مقاله:
Hamidreza Rashidy Kanan - ۱Electrical Engineering Department, AmirKabir University of Technology, Hafez Avenue, Tehran, Iran, ۱۵۹۱۴
Karim Faez
Saeed Mozaffari
خلاصه مقاله:
Hamidreza Rashidy Kanan - ۱Electrical Engineering Department, AmirKabir University of Technology, Hafez Avenue, Tehran, Iran, ۱۵۹۱۴
Karim Faez
Saeed Mozaffari
This paper presents a system for off-line recognition of segmented (isolated) handwritten Farsi/Arabic characters and numerals. We have used Zernike Moment Invariant and Fractal Codes as two different kinds of features in this system. Also Radial Basis Function (RBF) neural network that is used for many engineering problems and pattern recognition tasks has been employed in this work. Simulation results on our database, which were gathered from various people with different ages and different educational backgrounds, indicate that the ZMI and fractal codes are suitable features for segmented handwritten Farsi/Arabic characters and numerals recognition and the best performances of this system are 91.5% and 92.8% for characters and numerals recognition respectively
صفحه اختصاصی مقاله و دریافت فایل کامل: https://civilica.com/doc/150462/