The most important intricacy when processing natural scene text images is the existence of fog, smoke or haze. These intrusion elements decrease the contrast and disrupt the color fidelity of the image for various computer vision applications. In this paper, such a challenging issue is addressed. The intended work presents a novel method, that is, single image dehazing, based on transmission map. The contributions are performed in the following ways: (1) text extraction from hazy image is not straightforward due to lack of haze-free images and hazy images. To address this limitation, we introduce synthetic natural scene text image composed of pairs of synthetic hazy and corresponding haze-free images using mainstream datasets. Different from existing dehazing datasets, text in hazy images is considered compulsory content, which needs to be separated from background using the recovered image. For doing this, based on transmission map the scenic depth is calculated using haze density and color attenuation to generate depth map. In the next step, raw transmission map is computed, which is further refined using bilateral filtering to preserve edges and avoid possible noise; (2) text region proposals are estimated on the restored images using novel low-level connected component technique and character bounding is employed to complete the process. Finally, the experimentations are carried out on the images selected from standard datasets including MSRA-TD500, SVT and KAIST. The experimental outcomes demonstrate that the intended method performs better when compared with benchmark standard techniques and publically available dehazing datasets.

中文翻译：

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从合成的朦胧自然图像中提取场景文本的新方法

处理自然场景文本图像时，最重要的复杂性是雾气，烟雾或阴霾的存在。这些入侵元素会降低对比度，并破坏各种计算机视觉应用程序的图像色彩保真度。在本文中，解决了这个具有挑战性的问题。预期的工作提出了一种新颖的方法，即基于透射图的单图像去雾。这些贡献是通过以下方式执行的：（1）由于缺少无雾图像和模糊图像，从模糊图像中提取文本并不容易。为了解决这个限制，我们使用主流数据集介绍了由自然模糊和相应的无雾图像对组成的合成自然场景文本图像。与现有的除雾数据集不同，朦胧图像中的文本被视为必填内容，需要使用恢复的图像将其与背景分开。为此，基于透射图，使用雾度密度和颜色衰减来计算风景深度，以生成深度图。下一步，计算原始传输图，使用双边滤波进一步完善原始传输图，以保留边缘并避免可能的噪声；（2）使用新颖的低级连接组件技术在还原的图像上估计文本区域建议，并使用字符边界完成此过程。最后，对选自标准数据集（包括MSRA-TD500，SVT和KAIST）的图像进行了实验。实验结果表明，与基准标准技术和公开可用的除雾数据集相比，预期的方法性能更好。