Persian Handwritten Character Recognition Using Convolutional Neural Network

被引：0

作者：

Sarvaramini, Farzin ^{[1
]}

Nasrollahzadeh, Alireza ^{[1
]}

Soryani, Mohsen ^{[1
]}

机构：

[1] Iran Univ Sci & Technol, Fac Comp Engn, Tehran, Iran

来源：

26TH IRANIAN CONFERENCE ON ELECTRICAL ENGINEERING (ICEE 2018) | 2018年

关键词：

Visual pattern recognition; Deep learning; Convolutional neural network; Persian handwritten character recognition;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

While there has been some fascinating researches in Persian handwritten character recognition, there is still high demand for approaches with more accurate results. In order to achieve this goal, we use a deep learning architecture, in general, and Convolutional Neural Networks (CNN) in particular, for automatic feature extraction which is different from conventional hand-crafted feature extraction. Recently CNNs have solved many problems in sophisticated computer vision tasks. In the presented model, some optimization methods are used in order to enhance the performance of the CNN. The proposed model was trained and tested using the HODA dataset resulting an accuracy of 97.7%.

引用

页码：1676 / 1680

页数：5