Persian
Handwritten Digit Recognition Using Combination of Convolutional Neural Network
and Support Vector Machine Methods
Mohammad Parseh, Mohammad
Rahmanimanesh, and Parviz Keshavarzi
Faculty
of Electrical and Computer Engineering, Semnan University, Iran
Abstract: Persian
handwritten digit recognition is one of the important topics of image
processing which significantly considered by researchers due to its many
applications. The most important challenges in Persian handwritten digit
recognition is the existence of various patterns in Persian digit writing that
makes the feature extraction step to be more complicated.Since the handcraft
feature extraction methods are complicated processes and their performance
level are not stable, most of the recent studies have concentrated on proposing
a suitable method for automatic feature extraction. In this paper, an automatic
method based on machine learning is proposed for high-level feature extraction from
Persian digit images by using Convolutional Neural Network (CNN). After that, a
non-linear multi-class Support Vector Machine (SVM) classifier is used for data
classification instead of fully connected layer in final layer of CNN. The
proposed method has been applied to HODA dataset and obtained 99.56% of
recognition rate. Experimental results are comparable with previous
state-of-the-art methods.
Keywords:
Handwritten Digit Recognition, Convolutional Neural Network, Support Vector
Machine.
Received January 1,
2019; accepted November 11, 2019