Classification of Handwritten Chinese Numbers with Convolutional Neural Networks
Classification of Handwritten Chinese Numbers with Convolutional Neural Networks
复制标题
用卷积神经网络对手写中文数字进行分类
DOI：
10.1109/ipria53572.2021.9483557
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Ameri R
中科院分区：
文献类型：
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作者：
Ameri R
深度学习方法已经成为计算机视觉领域的关键组成部分，尤其是卷积神经网络(CNN)。适当的网络架构和数据预处理对性能有很大的影响。本文主要研究手写汉字数字的分类问题。首先，我们对采集到的图像数据集进行了各种方法的预处理。其次，我们定制了一个基于CNN的架构，具有最少的层数和专门针对该任务的参数。实验结果表明，该方法具有99.1%的分类正确率。实验结果还表明，与参数较少的较小神经网络(如Squeezenet)和参数较多的较深网络(如GoogLeNet和MobileNetV2)相比，该方法具有相当的性能。
Deep learning methods have become the key ingredient in the field of computer vision; in particular, convolutional neural networks (CNNs). Appropriating the network architecture and data pre-processing have significant impact on performance. This paper focuses on the classification of handwritten Chinese numbers. Firstly, we applied various methods of pre-processing to our collected image dataset. Secondly, we customised a CNN-based architecture with minimal number of layers and parameters specifically for the task. Experimental results showed that our proposed methods provides superior classification rate of 99.1%. Our results also show that the proposed method has competitive performance compared to smaller neural networks with fewer parameters, e.g. Squeezenet and deeper networks with a larger size and number of parameters, e.g., pre-trained GoogLeNet and MobileNetV2.