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Text-Line Detection in Camera-Captured Document Images Using the State Estimation of Connected Components

机译:使用连接的组件的状态估计在摄像机捕获的文档图像中进行文本行检测

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摘要

Camera-based text processing has attracted considerable attention and numerous methods have been proposed. However, most of these methods have focused on the scene text detection problem and relatively little work has been performed on camera-captured document images. In this paper, we present a text-line detection algorithm for camera-captured document images, which is an essential step toward document understanding. In particular, our method is developed by incorporating state estimation (an extension of scale selection) into a connected component (CC)-based framework. To be precise, we extract CCs with the maximally stable extremal region algorithm and estimate the scales and orientations of CCs from their projection profiles. Since this state estimation facilitates a merging process (bottom-up clustering) and provides a stopping criterion, our method is able to handle arbitrarily oriented text-lines and works robustly for a range of scales. Finally, a text-lineon-text-line classifier is trained and non-text candidates (e.g., background clutters) are filtered out with the classifier. Experimental results show that the proposed method outperforms conventional methods on a standard dataset and works well for a new challenging dataset.
机译:基于照相机的文本处理已经引起了相当大的关注,并且已经提出了许多方法。但是,这些方法中的大多数都集中在场景文本检测问题上,并且对相机捕获的文档图像执行的工作相对较少。在本文中,我们提出了一种用于相机捕获的文档图像的文本行检测算法,这是迈向文档理解的重要步骤。特别是,我们的方法是通过将状态估计(规模选择的扩展)合并到基于连接组件(CC)的框架中而开发的。确切地说,我们使用最大稳定的极值区域算法提取CC,并从CC的投影轮廓估计CC的比例和方向。由于此状态估计有助于合并过程(自下而上的聚类)并提供停止条件,因此我们的方法能够处理任意定向的文本行,并且可以在一定范围内稳定运行。最后,训练文本行/非文本行分类器,并使用分类器过滤掉非文本候选者(例如背景杂波)。实验结果表明,该方法在标准数据集上优于传统方法,并且对于新的具有挑战性的数据集而言效果很好。

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