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首页> 外文会议>International Conference on Asian Digital Libraries(ICADL 2004); 20041213-17; Shanghai(CN) >Character Region Identification from Cover Images Using DTT
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Character Region Identification from Cover Images Using DTT

机译：使用DTT从封面图像识别字符区域

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摘要

A robust character region identification approach is proposed here to deal with cover images using a differential top-hat transformation (DTT). The DTT is derived from morphological top-hat transformation (TT), and efficient for feature identification. This research is considered as a fundamental study for auto-classification of printed documents for organizing a Digital Library (DL) system. The entire procedure can be divided into two steps: region classification and character region identification. In the first step, a source gray image is segmented by a series of structuring elements (SE) into sub-images using the DTT. Since the widths of regions are relative to the scales of the characters, the different scales of characters are classified into the series of sub-images. The character region identification processing is composed of feature emphasis, extraction of candidate character regions and region reconstruction processing. Feature emphasis processing reduces noises and emphasizes characters in the sub-images, and then the candidate character regions are extracted from the gray scale sub-images by a histogram analysis. Lastly, a morphological image reconstruction algorithm based on conditional dilation is introduced to make the extracted character regions distinct from noises. To demonstrate the robustness of the proposed approach, 30 gray scale cover images were tested in the experiments, which revealed that an average extraction rate of 94% has been achieved.
机译：在此提出了一种鲁棒的字符区域识别方法，以使用差分礼帽变换（DTT）处理封面图像。 DTT从形态学礼帽变换（TT）派生而来，对于特征识别非常有效。这项研究被认为是对打印文档进行自动分类以组织数字图书馆（DL）系统的基础研究。整个过程可以分为两个步骤：区域分类和字符区域识别。第一步，使用DTT将源灰度图像通过一系列结构元素（SE）分割为子图像。由于区域的宽度与字符的比例有关，因此将不同比例的字符分类为一系列子图像。字符区域识别处理由特征强调，候选字符区域的提取和区域重构处理组成。特征强调处理减少了噪声并强调了子图像中的字符，然后通过直方图分析从灰度子图像中提取候选字符区域。最后，提出了一种基于条件膨胀的形态学图像重建算法，以使提取的字符区域与噪声区分开。为了证明所提出方法的鲁棒性，在实验中测试了30张灰度封面图像，结果表明已实现94％的平均提取率。

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