特征分组提取融合深度网络手写汉字识别

Feature Grouping Extraction Fusion of Deep Network Offline Handwritten Chinese Character Recognition

针对传统脱机手写汉字识别的过程复杂、精度低,而常用卷积神经网络的特征信息提取不充分,同时存在相同特征信息的重叠和冗余问题。设计了一个特征分组提取融合的深度卷积神经网络模型。通过多级堆叠的特征分组提取模块,提取图像的深层抽象特征信息,并进行特征信息之间的交流融合。利用设计的下采样和通道扩增模块,在降低特征维度的同时保留图像重要信息。将特征信息进行精炼和浓缩,来解决特征信息的重叠和冗余问题。最终训练出的神经网络达到top1当前先进的正确率为97.16%,同时top5正确率为99.36%,并具有很好的泛化能力。

For traditional offline handwritten Chinese character recognition,the process is complex and the accuracy is low.The feature information extraction of common convolutional neural networks is insufficient,and there are overlap and redundancy problems of the same feature information.In this paper,a deep convolutional neural network model for feature group extraction and fusion is designed.Through the multi-level stacked feature group extraction module,the deep abstract feature information of the image is extracted,and the communication and fusion between the feature information is performed.Using the designed downsampling and channel amplification module to preserve the important information of the image while reducing the feature dimension.The feature information is refined and condensed to solve the problem of overlapping and redundancy.In the end,the trained neural network reaches the current advanced accuracy of top1(97.16%)and top5(99.36%),with good generalization ability.