Evaluation of Open-Source OCR Libraries for Scene Text Recognition in the Presence of Fisheye Distortion

Abstract

Due to the rich and precise semantic information that text provides, scene text recognition is relevant in a wide range of vision-based applications. In recent years, the use of vision systems that combine a camera and a fisheye lens is common in a variety of applications. The addition of a fisheye lens has the great advantage of capturing a wider field of view, but this causes a great deal of distortion, making certain tasks challenging. In many applications, such as localization or mapping for a mobile robot, the algorithms work directly with fisheye images (i.e. distortion is not corrected). For this reason, the principal objective of this work is to study the effectiveness of some OCR (Optical Character Recognition) open-source libraries applied to images with fisheye distortion. Since no scene text dataset of this kind of image has been found, this work also generates a synthetic image dataset. A fisheye model which varies some parameters is applied to standard images of a benchmark scene text dataset to generate the proposed dataset.