چکیده مقاله
Optical character recognition OCR1 is one of the most commonly used topics in field of image processing, which has undergone many researches in this today The OCR includes three main steps, preprocessing, feature extraction and learning In preprocessing, normalization of images and changing them in to monochrome image version are done In feature extraction, for each sample a feature vector is created which represents the main attributes of that sample image In the learning step, multilayer perceptron neural network that is trained with samples a set of handwritten Persian/Arabic digits match the input test images to the digits In this paper presents a method for persian/arabic handwritten digit recognition using neural network The proposed system consists of three phases, preprocessing, feature extraction and learning features Learning step is done by multilayer perceptron neural network MLP2 and a training set of digit image samples That uses to detect Persian/Arabic digits The proposed method are tested by MAHDBase database and the experimental results shows that the proposed algorithm has a good accuracy
کلیدواژهها
نویسندگان
Seyedeh Hamideh Erfani
Engineering Department of Damghan university
Mansoureh Maadi
Engineering Department of Damghan university
شیوه ارجاع
Erfani, Seyedeh Hamideh and Maadi, Mansoureh,1396,Persian/Arabic Handwritten Digit Recognition Using Neural Network,2rd International Conference on Soft Computing,Rudsar
ارائهشده در
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