Recognition of Handwritten Persian/Arabic Numerals Based on Robust Feature Set and K-NN Classifier
سال انتشار: 1392
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 411
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شناسه ملی سند علمی:
JR_IJOCIT-1-3_004
تاریخ نمایه سازی: 16 فروردین 1395
چکیده مقاله:
Persian handwritten numerals recognition has been a frontier area of research for the last few decades under pattern recognition. Recognition of handwritten numerals is a difficult task owing to various writing styles of individuals. A robust and efficient method for Persian/Arabic handwritten numerals recognition based on K Nearest Neighbors (K-NN) classifier is presented in this paper. The system first prepares a contour form of the handwritten numerals, then the transit, angle and distance features information about the character is extracted and finally K-NN classifier is used to character recognition. Angle, transit and distance features of a character have been computed based on distribution of points on the bitmap image of character. In K-NN method, the Euclidean distance between testing point and reference points is calculated in order to find the k-nearest neighbors. We evaluated our method on 20,000 handwritten samples of Persian numerals. Using 15,000 samples for training, we tested our method on other 5,000 samples and obtained 99.82% correct recognition rate. Further, we obtained 89.90% accuracy using four-fold cross validation technique on 20,000 dataset
کلیدواژه ها:
نویسندگان
Reza Azad
Electrical and Computer Engineering Department Shahid Rajaee Teacher TrainingUniversity, Tehran, Iran
Fatemeh Davami
Electrical and Computer Engineering Department Firoozabad Branch, Meymand Center, Islamic Azad University Meymand, Iran
Hamidreza Shayegh Boroujeni
Electrical and Computer Engineering Department Shahid Rajaee Teacher TrainingUniversity, Tehran, Iran