• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Pattern recognition letters >An adaptive local binarization method for document images based on a novel thresholding method and dynamic windows
【24h】

An adaptive local binarization method for document images based on a novel thresholding method and dynamic windows

机译：基于新颖阈值法和动态窗口的文档图像自适应局部二值化方法

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Binary image representation is essential format for document analysis. In general, different available binarization techniques are implemented for different types of binarization problems. The majority of binarization techniques are complex and are compounded from filters and existing operations. However, the few simple thresholding methods available cannot be applied to many binarization problems. In this paper, we propose a local binarization method based on a simple, novel thresholding method with dynamic and flexible windows. The proposed method is tested on selected samples called the DIBCO 2009 benchmark dataset using specialized evaluation techniques for binarization processes. To evaluate the performance of our proposed method, we compared it with the Niblack, Sauvola and NICK methods. The results of the experiments show that the proposed method adapts well to all types of binarization challenges, can deal with higher numbers of binarization problems and boosts the overall performance of the binarization.
机译：二进制图像表示是文档分析的基本格式。通常，针对不同类型的二值化问题实施了不同的可用二值化技术。大多数二值化技术很复杂，并且与过滤器和现有操作混合在一起。但是，几种可用的简单阈值化方法不能应用于许多二值化问题。在本文中，我们提出了一种基于简单，新颖，具有动态和灵活窗口的阈值化方法的局部二值化方法。使用针对二值化过程的专用评估技术，对所选样本（称为DIBCO 2009基准数据集）进行了测试。为了评估我们提出的方法的性能，我们将其与Niblack，Sauvola和NICK方法进行了比较。实验结果表明，该方法能够很好地适应所有类型的二值化挑战，可以处理更多数量的二值化问题，提高了二值化的整体性能。

著录项

  • 来源
    《Pattern recognition letters》 |2011年第14期|p.1805-1813|共9页
  • 作者

    Bilal Bataineh; Siti Norul Huda Sheikh Abdullah; Khairuddin Omar;

  • 作者单位

    Center for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia;

    rnCenter for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia;

    rnCenter for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    document image binarization; local binarization; OCR; text; thresholding methods;

    机译：文档图像二值化;本地二值化;OCR;文本;阈值方法;

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号