A New Design Based-SVM of the CNN Classifier Architecture with Dropout for Offline Arabic Handwritten Recognition

被引:126
作者
Elleuch, Mohamed [1 ]
Maalej, Rania [2 ]
Kherallah, Monji [3 ]
机构
[1] Univ Manouba, Natl Sch Comp Sci ENSI, Manouba, Tunisia
[2] Univ Sfax, Natl Sch Engineers ENIS, Sfax, Tunisia
[3] Univ Sfax, Fac Sci, Sfax, Tunisia
来源
INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE 2016 (ICCS 2016) | 2016年 / 80卷
关键词
CNN; dropout; Arabic handwritten recognition; over-fitting; based-SVM; features; HACDB; SUPPORT VECTOR MACHINES; PATTERN-RECOGNITION; NETWORKS;
D O I
10.1016/j.procs.2016.05.512
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
In this paper we explore a new model focused on integrating two classifiers; Convolutional Neural Network (CNN) and Support Vector Machine (SVM) for offline Arabic handwriting recognition (OAHR) on which the dropout technique was applied. The suggested system altered the trainable classifier of the CNN by the SVM classifier. A convolutional network is beneficial for extracting features information and SVM functions as a recognizer It was found that this model both automatically extracts features from the raw images and performs classification. Additionally, we protected our model against over-fitting due to the powerful performance of dropout. In this work, the recognition on the handwritten Arabic characters was evaluated; the training and test sets were taken from the HACDB and IFN/ENIT databases. Simulation results proved that the new design based-SVM of the CNN classifier architecture with dropout performs significantly more efficiently than CNN based-SVM model without dropout and the standard CNN classifier. The performance of our model is compared with character recognition accuracies gained from state-of-the-art Arabic Optical Character Recognition, producing favorable results.
引用
收藏
页码:1712 / 1723
页数:12
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