一种工件表面压印字符识别网络

doi:10.13229/j.cnki.jdxbgxb.20230150

Abstract

Abstract:

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 anchor-free 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.

Key words: imprint character, character recognition, anchor-free, small sample, object detection

CLC Number:

TP391

Xin-dong YOU,Lei GUO,Jing HAN,Xue-qiang LYU. An character recognition network for imprint character[J].Journal of Jilin University(Engineering and Technology Edition), 2024, 54(7): 2072-2079.

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References 16

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数据集	算法	准确率	召回率	F₁值
自建螺栓数据集	Faster R-CNN	0.975	0.962	0.969
	YOLOv5	0.979	0.970	0.974
	CenterNet	0.980	0.949	0.964
	RetinaNet	0.943	0.906	0.924
	本文	0.996	0.997	0.996
SynthText数据集	Faster R-CNN	0.955	0.937	0.946
	YOLOv5	0.962	0.939	0.950
	CenterNet	0.945	0.920	0.932
	RetinaNet	0.898	0.886	0.891
	本文	0.973	0.963	0.968

数据集	算法	基线准确率	替换后准确率	基线帧率/（帧·s^-1）	替换后帧率/（帧·s^-1）
自建螺栓数据集	VGG16	0.963	0.974	112.7	113.9
自建螺栓数据集	ResNet50	0.975	0.981	95.1	95.8
SynthText数据集	VGG16	0.915	0.922	184.8	186.3
SynthText数据集	ResNet50	0.923	0.932	148.5	149.1

跨域迁移	无	有
自建螺栓数据集	0.981	0.990
SynthText数据集	0.932	0.949

算法	准确率	召回率	F₁值
Faster R-CNN	0.947	0.934	0.941
YOLOv5	0.967	0.956	0.962
CenterNet	0.961	0.931	0.946
RetinaNet	0.923	0.887	0.905
本文	0.985	0.986	0.985

算法	准确率	召回率	F₁值
Faster R-CNN	0.904	0.887	0.895
YOLOv5	0.915	0.893	0.903
CenterNet	0.877	0.864	0.870
RetinaNet	0.843	0.827	0.835
本文	0.923	0.914	0.918

An character recognition network for imprint character

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