计算机工程与应用2024，Vol.60Issue(24)：149-157,9.DOI:10.3778/j.issn.1002-8331.2308-0161
MBRNet:融合残差连接的多分支手写字符识别网络
MBRNet:Multi-Branch Handwritten Character Recognition Network with Integrated Residual Connection
摘要
Abstract
Offline handwritten Chinese character recognition(HCCR)has been a great challenge in the field of computer vision.Compared with traditional methods,deep learning-based networks have achieved differentiated results in the recog-nition task by training a large amount of data,but the recognition effect is still in the process of development.Based on this,a multi-branch residual block is designed by combining DW convolution operations and residual connections.In this block,DW convolution operations increase the depth of the network and enhance feature extraction capabilities at the cost of smaller memory usage and parameter count.And the residual connections facilitate data spiraling flow,effectively miti-gating gradient and degradation issues in the network.Furthermore,a multi-branch weight algorithm is proposed to address the weight allocation issue for the branches within the multi-branch residual block.Six multi-branch residual blocks are linearly connected to form the HCCR recognition network.The model achieves recognition accuracies of 97.77%,97.30%,and 97.64%on the CASIA-HWDB1.0,CASIA-HWDB1.1,and ICDAR2013 datasets,respectively,showing high recogni-tion accuracy.关键词
手写中文字符识别(HCCR)/多分支残差模块/DW卷积/残差连接/多分支权重Key words
handwritten Chinese character recognition(HCCR)/multi-branch residual block/DW convolution/residual connection/multi-branch weighting分类
信息技术与安全科学引用本文复制引用
李钢,陈太兵,杨之博,范屹,张玲..MBRNet:融合残差连接的多分支手写字符识别网络[J].计算机工程与应用,2024,60(24):149-157,9.基金项目
山西省中央引导地方专项基金(YDZJSX2021C004,YDZJSX20231C004) （YDZJSX2021C004,YDZJSX20231C004）
山西省自然科学基金(20210302124554). （20210302124554）
