Farsi Characterrecognitionusingnew Hybrid Featureextractionmethods

Identification of visual words and writings has long been one of the most essential and the most attractive operations in the field of image processing which has been studied since the last few decades and includes security, traffic control, fields of psychology, medicine, and engineering, etc. Previous techniques in the field of identification of visual writings are very similar to each other for the most parts of their analysis, and depending on the needs of the operational field have presented different feature extraction. Changes in style of writing and font and turns of words and other issues are challenges of characters identifying activity. In this study, a system of Persian character identification using independent orthogonal moment that is Zernike Moment and Fourier-Mellin Moment has been used as feature extraction technique. The values of Zernike Moments as characteristics independent of rotation have been used for classification issues in the past and each of their real and imaginary components have been neglected individually and with the phase coefficients, each of them will be changed by rotation. In this study, Zernike and FourierMellin Moments have been investigated to detect Persian characters in noisy and noise-free images. Also, an improvement on the k-Nearest Neighbor (k-NN) classifier is proposed for character recognition. Using the results comparison of the proposed method with current salient methods such as Back Propagation (BP) and Radial Basis Function (RBF) neural networks in terms of feature extraction in words, it has been shown that on the Hoda database, the proposed method reaches an acceptable detection rate (96/5%).