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首页> 外文会议>International Conference on Information Science, Signal Processing and Their Applications >IMAGE PATCHES ANALYSIS FOR TEXT BLOCK IDENTIFICATION
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IMAGE PATCHES ANALYSIS FOR TEXT BLOCK IDENTIFICATION

机译：文本块识别的图像修补分析

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摘要

In this paper, we propose a novel text block identification method for ancient document understanding. Unlike traditional top-down and bottom-up approaches, our method is based on supervised learning on the patches of document images, which can be considered as an intermediate level method but integrates essential advantages of both the top-down and the bottom-up strategies. In our method, the document images are firstly partitioned into small patches, and then positive and negative patches are selected to form an active training set. Gabor features are extracted on each patch, while multi-linear discriminant analysis (MDA) is employed to reduce the dimensionality of the data. To deal with unseen documents, a random forest classifier is learned on the new representations of the patches. Compared to traditional approaches, our method can not only capture local texture features of each patch, but also preserve the global information of the training images. Furthermore, MDA is guaranteed to learn a low dimensional tensor subspace, which significantly avoids the curse of dimensionality dilemma. Moreover, the random forest classifier can automatically select useful features and deliver satisfactory identification results. Extensive experiments on some scripts of ancient document images demonstrated the effectiveness of our method.
机译：在本文中，我们提出了一种用于古代文献理解的新型文本块识别方法。与传统的自上而下和自下而上的方法不同，我们的方法是基于对文档图像斑块的监督学习，这可以被视为中间水平方法，而是整合自上而下和自下而上策略的基本优势。在我们的方法中，文档图像首先将正面划分为小补丁，然后选择正和否定斑块以形成活动训练集。在每种贴片上提取Gabor特征，而使用多线性判别分析（MDA）以降低数据的维度。要处理看不见的文件，可以在补丁的新表现中了解一个随机林分类器。与传统方法相比，我们的方法不仅可以捕获每个补丁的本地纹理特征，还可以保留培训图像的全局信息。此外，MDA保证学习低维张力子空间，这显着避免了维度困境的诅咒。此外，随机林分类器可以自动选择有用的功能并提供令人满意的识别结果。对古代文件图像的一些脚本的广泛实验表明了我们方法的有效性。

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