A novel approach for scene text extraction from synthesized hazy natural images

Abstract

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.