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

脱机手写中文字符识别(handwritten Chinese character recognition,HCCR)在计算机视觉领域一直是一个巨大的挑战。相比传统方法,基于深度学习的网络通过训练大量数据在识别任务中取得了差异化的效果,但识别效果依旧处于发展过程中。基于此,结合DW卷积和残差连接设计了一种多分支残差模块,该模块通过DW卷积以较小的内存和参数量为代价来加深网络深度,增强网络的特征提取能力;再通过残差连接抑制网络梯度问题和退化问题;另外,提出了一种多分支权重算法,来改善多分支残差模块中各分支的权重分配问题;并将六个以多分支残差模块为主的结构线性连接,组成HCCR识别网络。该模型在CASIA-HWDB1.0、CASIA-HWDB1.1、ICDAR2013数据集上的识别准确率分别达到了97.77%、97.30%、97.64%,表现出高精度的识别效果。

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.