Offline Persian letters recognition based on Histogram of Oriented Gradients features in image

سال انتشار: 1397
نوع سند: مقاله کنفرانسی
زبان: انگلیسی
مشاهده: 424

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شناسه ملی سند علمی:

IDMEC01_006

تاریخ نمایه سازی: 29 بهمن 1398

چکیده مقاله:

The target of this work is discrimination of Persian alphabet from each other. Hence, in first step, we extract features from images that encode image regions as high dimensional feature vectors that support high accuracy decisions. The required features are extracted from Histograms of Oriented Gradients (HOG). In second step, we use different classifier for learning and testing alphabet classes with partially labeled data which are support vector machine (SVM) with different kernels, K nearest neighborhood (KNN), naïve byes (NB), parabolic neural network (PNN). In the third step, we collect data form 10 Persian people. 5 Persian girl and 5 Persian boys wrote 10 Persian alphabets in paper.After that we created scanning of the handwritten papers to collect alphabet database. We select 70 percent of data for training and 30 percent of data for testing part. Our experimental results show the optimal performance in accuracy, recall, precision and confusion matrix

نویسندگان

Hossein Soleimani

Department of Electrical and Computer Engineering, Faculty of GhaziTabatabai, Urmia branch, Technical and Vocational University, Urmia, Iran

Ghollamali Alizadeh

Department of Electrical and Computer Engineering, Faculty of GhaziTabatabai, Urmia branch,