对于产品代码识别的机器视觉方法的最新进展:系统审查
Jarmo Koponen1, Keijo Haataja1, Pekka Toivanen1
1School of Computing, Kuopio campus, University of Eastern Finland, Kuopio, Pohjois-Savo, FI-70211, Finland.
F1000Research
|September 28, 2023
概括
对于产品文本识别的机器视觉有了显著的进步,深度学习神经网络 (DNN) 现在克服了阅读包装上的制造标记的常见挑战. 这种进化改善了制药和食品产品的处理.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 模式识别 模式识别
背景情况:
- 制造标记对于处理药品和易腐食品至关重要.
- 目前的方法包括光学字符识别 (OCR) 和神经网络.
- 识别这些文本在现实世界的应用中带来了重大挑战.
研究的目的:
- 在8年的时间里 (2012-2020年) 系统地审查产品文本识别的机器视觉方法的演变.
- 确定在产品表面识别文本时遇到的常见挑战.
- 评估识别技术的性能和发展.
主要方法:
- 对2012年至2020年间发表的文章进行系统的文献综述.
- 搜索的数据库:科学直接/SCOPUS和谷歌学者.
- 包括详细介绍识别方法,性能,成像,产品类型和文本的研究.
主要成果:
- 产品文本识别方法取得了重大进展,特别是在审查期间的最后两年.
- 深度学习神经网络 (DNN) 架构,像卷积神经网络 (CNN),擅长从包装图像中提取特征.
- 最近的方法采用双网络方法来检测文本区域和字符识别,解决常见的困难.
结论:
- 有限的研究符合审查标准,重点是用于制造品牌识别的机器视觉.
- 方法从OCR演变为先进的深度学习方法.
- 深度学习能够实现强大的文本识别,尽管常见的行业挑战.
关键词:
字符识别 字符识别深度学习 (Deep Learning) 是一种深度学习.图像系统的成像系统机器视觉 机器视觉 机器视觉制造业 制造业 制造业 制造业在OCR中,OCR是OCR.包装 包装是一种包装.产品 产品 产品 产品 产品更多相关视频
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