Adaptive Local Receptive Field Convolutional Neural Networks for Handwritten Chinese Character Recognition

摘要：

　　The success of convolutional neural networks (CNNs) in the field of image recognition suggests that local connectivity is one of the key issues to exploit the prior information of structured data.But the problem of selecting optimal local receptive field still remains.We argue that the best way to select optimal local receptive field is to let CNNs learn how to choose it.To this end,we first use different sizes of local receptive fields to produce several sets of feature maps,then an element-wise max pooling layer is introduced to select the optimal neurons from these sets of feature maps.A novel training process ensures that each neuron of the model has the opportunity to be fully trained.The results of the experiments on handwritten Chinese character recognition show that the proposed method significantly improves the performance of traditional CNNs.

关键词： Convolutional Neural Networks (CNNs) Local Receptive Field Handwritten Chinese Character Recognition

作者: Li Chen Chunpeng Wu Wei Fan Jun Sun Naoi Satoshi

作者单位: Fujitsu Research & Development Center Co. Ltd.,Beijing,100025,China

会议类型: 国际会议

会议名称: Chinese Conference on Pattern Recognition, CCPR(2014年全国模式识别学术会议)

会议地点: 长沙

会议语种:英文

页码: 455-463

在线出版日期: 2014-11-01（万方平台首次上网日期，不代表论文的发表时间）