基于轻量级UNet的复杂背景字符语义分割网络

Semantic segmentation network for complex background characters based on lightweight UNet

针对传统复杂背景字符分割算法的不足,提出了一种基于轻量级UNet的复杂背景字符语义分割网络.网络结构基于UNet,在特征提取模块中,将传统卷积变为深度可分离卷积,减少了网络特征提取模块的参数量以及计算量,并引入残差学习模块解决网络退化问题.在自制数据集以及H-DIBCO2018公开数据集上展开实验,并与FCN8s、AttationUNet和UNet进行比较.实验结果表明:所提出的网络可同时兼顾计算效率与分割精度,具有实用性.

Towards the problems of traditional complex background character segmentation algorithm,a semantic segmentation network for complex background characters based on lightweight UNet is proposed.The network structure is based on UNet.In the feature extraction module,the traditional convolution is changed into deepthwise separable convolution,which greatly reduces the number of parameters and computation of the network feature extraction module.The residual learning module is introduced to solve the network degradation problem.Experiments were performed on the self-made dataset and H-DIBCO2018 open dataset,and compared with FCN8s,AttationUNet and UNet.Experimental results show that the proposed network has both computational efficiency and segmentation accuracy,and is practical.