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HCCD:一个手写的摄像头捕获的数据集,用于在各种降解条件下对文档进行增强
K S Koushik1, Bipin Nair B J1, N Shobha Rani2
1Department of Computer Science, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Mysuru, India.
Data in brief
|July 23, 2025
概括
一个新的数据集,HCCD,解决了通过智能手机捕获的恶化手写文档增强的挑战. 这种资源有助于深度学习模型在现实世界的图像恢复和OCR任务.
科学领域:
- 计算机视觉 计算机视觉
- 文件分析 文件分析
- 机器学习 机器学习
背景情况:
- 从智能手机图像中增强退化的手写文档是一项挑战.
- 深度学习模型需要广泛的标记数据集来进行有效的训练.
- 现有的数据集往往缺乏现实世界的退化复杂性.
研究的目的:
- 介绍手写摄像头捕获的数据集 (HCCD).
- 提供一个数据集用于训练深度学习模型在现实世界手写文档增强.
- 支持图像修复,无色化和光学字符识别 (OCR) 的研究.
主要方法:
- 收集实时的,摄像机捕捉的手写文档与自然退化 (运动模糊,阴影,不均的照明).
- 用计算机视觉技术为每个文件创建了高质量的增强图像.
- 附注样本与元数据,包括退化类型,严重程度和作家人口统计.
主要成果:
- 该HCCD数据集具有多样化的,自然降解的手写文档.
- 配对图像允许对增强和识别模型进行监督学习.
- 元数据可以针对特定的降解因子和写作者的变异进行有针对性的研究.
结论:
- HCCD弥合了现实世界手写文档数据集的差距.
- 该数据集有助于开发用于文档分析的强大深度学习模型.
- 在实际应用中,HCCD支持图像恢复和OCR方面的进步.
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