一种工件表面压印字符识别网络  

An character recognition network for imprint character

作　　者：游新冬 郭磊 韩晶 吕学强[1] YOU Xin-dong;GUO Lei;HAN Jing;LYU Xue-qiang(Beijing Key Laboratory of Internet Culture and Digital Communication,Beijing Information Science and Technology University,Beijing 100101,China)

机构地区：[1]北京信息科技大学网络文化与数字传播北京市重点实验室,北京100101

出　　处：《吉林大学学报（工学版）》2024年第7期2072-2079,共8页Journal of Jilin University:Engineering and Technology Edition

基　　金：国家自然科学基金项目(62171043);北京市自然科学基金项目(4212020)。

摘　　要：工件表面的压印字符存在凹凸不平、锈蚀、风化等问题,导致传统的字符识别算法难以取得满意的效果。针对这一问题,将工件表面压印字符的识别视为一类特殊的目标检测问题,并针对其特性设计了一种两阶段识别网络:定位-分类网络。定位网络使用无锚框的方法提取字符感兴趣区域,有效解决了字符区域提取困难的问题。分类网络采用特征解耦的卷积模块和结构重参数化技术,能够在不增加额外参数的情况下提升分类的准确率。此外,分类网络采用跨域迁移学习的训练策略,能够有效解决实际应用中的小样本和类别不平衡问题。在自建螺栓数据集和SynthText数据集上的实验结果表明,该算法的整体精度能够达到98%和92%,优于对比算法。The imprint characters on the surface of the workpiece are uneven,rusty,and weathered,which the traditional character recognition methods hard to achieve satisfactory results.This paper regards the characters recognition task as a particular detection problem and designs a two-stage recognition network according to its characteristics:location and classification network.The location newtork uses the anchorfree method to extract the region of interest of characters,which effectively solves the problem of character region extraction.The classification network uses the Feature Decoupled Convolution Block and the Structural Re-parameterization technology,which can significantly improve the classification accuracy without any extra parameter.The transferring learning is used to solve the small sample problem and imbalance problem in the training stage.The experimental results on the self-built bolt dataset and the SynthText dataset show that the algorithm can achieve overall accuracies of 98%and 92%,respectively,which is superior to the compared algorithms.

关 键 词：压印字符 字符识别 无锚框 小样本 目标检测 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]