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An Efficient System for Hazy Scene Text Detection using a Deep CNN and Patch-NMS

机译:使用深CNN和Patch-NMS的朦胧场景文本检测的高效系统

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Scene text detection systems detect texts in natural scene images. Hazy scene text detection is a specific case of scene text detection where detection is done in hazy weather conditions. Haze affects the contrast of the image. In this paper, we reframe the traditional two class hazy scene text detection problem into a four class problem. We develop a deep learning based model that combines features from all layers for accurate and fast text detection from hazy images. In addition, we develop a novel training approach for the four class problem. Merging and patch-NMS are used as post processing steps for fast word detection. We also create a new dataset of hazy scene images and obtain significant improvements on an existing hazy scene text dataset.
机译:场景文本检测系统检测自然场景图像中的文本。朦胧场景文本检测是场景文本检测的具体情况,其中检测是在朦胧的天气条件下完成的。雾度影响图像的对比度。在本文中,我们将传统的两类朦胧场景文本检测问题重新描述为四类问题。我们开发了一个基于深度学习的模型,将所有层的功能组合出来自朦胧图像的准确和快速文本检测。此外,我们为四类问题开发了一种新的培训方法。合并和Patch-NMS用作快速字检测的后处理步骤。我们还创建了一个朦胧场景图像的新数据集,并在现有的朦胧场景文本数据集上获取显着的改进。

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