Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

HP1γ promotes the progression of colorectal cancer through interaction with RBPJ of the Notch signaling pathway.

Cell biology and toxicology·2026
Same author

α-Galactosylceramide-expanded virtual memory CD8<sup>+</sup> T cells confer protection against a broad range of pathogens.

Frontiers in immunology·2026
Same author

The Mechanism of NLRP3 Inflammasome Activation and Its Roles in Chronic Rhinosinusitis.

World journal of otorhinolaryngology - head and neck surgery·2026
Same author

The Ideal Number of Examined Lymph Node Stations for Accurate Nodal Staging and Prognosis in Pathological T1-2 Esophageal Squamous Cell Carcinoma: A Large-Scale Multicenter Cohort Study.

Annals of surgical oncology·2026
Same author

Thoracic endovascular aortic repair for high-risk Stanford type B aortic dissection in a 15-year-old patient with genetic mutations: a case report with follow-up of four years.

Cardiology in the young·2026
Same author

A multicenter randomized controlled trial evaluating a new radiofrequency ablation system in the treatment of primary great saphenous vein incompetence: Six-month results of the ACOART RF CLOSURE study.

Phlebology·2026

相关实验视频

Updated: Sep 17, 2025

Author Spotlight: Quantitative Characterization of Liquid Photosensitive Bioink Properties for Continuous Digital Light Processing Based Printing
04:32

Author Spotlight: Quantitative Characterization of Liquid Photosensitive Bioink Properties for Continuous Digital Light Processing Based Printing

Published on: April 14, 2023

1.1K

基于深度学习的印刷文档布局分析和光学字符识别系统.

Dong-Lin Li1, Shih-Kai Lee2, Yin-Ting Liu2

  • 1Department of electrical engineering, National Taiwan Ocean University, Beining Rd., Keelung City, 202301, Taiwan. ericli@email.ntou.edu.tw.

Scientific reports
|July 3, 2025
PubMed
概括
此摘要是机器生成的。

本研究引入了用于文档分析和文本识别的深度学习系统. 它准确地在本地处理打印文档,将其转换为可编辑格式,如JSON.

关键词:
在美国,CNN是CNN.深度学习是一种深度学习.布局分析 布局分析在OCR中,OCR是OCR.这是一个YOLO YOLO.

更多相关视频

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

9.1K
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

4.1K

相关实验视频

Last Updated: Sep 17, 2025

Author Spotlight: Quantitative Characterization of Liquid Photosensitive Bioink Properties for Continuous Digital Light Processing Based Printing
04:32

Author Spotlight: Quantitative Characterization of Liquid Photosensitive Bioink Properties for Continuous Digital Light Processing Based Printing

Published on: April 14, 2023

1.1K
Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

9.1K
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

4.1K

科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能
  • 机器学习 机器学习

背景情况:

  • 印刷文档处理通常需要专门的软件.
  • 现有的光学字符识别 (OCR) 方法可能很慢或不准确.
  • 深度学习为改进文档分析提供了潜力.

研究的目的:

  • 开发一个高效,准确的深度学习系统,用于文档布局分析和文本识别.
  • 为了使扫描文件和图像文件的本地处理.
  • 以用户友好的,可编辑的格式输出识别的文本.

主要方法:

  • 使用YOLOv4和YOLOv8深度学习模型进行文档布局分析 (识别标题,段落,表格,图像).
  • 为每个已识别的文档元素实施了字符分割.
  • 使用卷积神经网络 (CNN) 进行文本识别.
  • 将已识别的文本集成到可编辑格式 (JSON,微软格式).

主要成果:

  • 能够准确识别文件元素,如标题,段落,表格和图像.
  • 通过CNNs在文本识别方面表现出高精度.
  • 在本地计算机上启用了快速方便的OCR处理.

结论:

  • 拟议的深度学习系统为文档布局分析和OCR提供了强大的解决方案.
  • 该方法可显著提高本地文件处理的速度和准确性.
  • 可编辑的输出格式提高了识别文本的可用性.