Multi-Oriented Text Detection in Natural Scene Images Based on the Intersection of MSER With the Locally Binarized Image

Abstract

Abstract The problem of text extraction is an interesting area of research in computer vision domain. In the recent years, emergence of various applications on smart hand-held devices such as translation of text from one language to another in real time, computerized aid for visually impaired, user navigation & traffic monitoring and driving assistance systems, has stimulated the renewed research interest in this domain. Retrieving text directly from natural scene images or videos is a challenging task due to variant patterns and orientations of scene text. Although various research investigations are available on horizontal oriented text detection in natural scene images, a little number of studies exist on text detection of multiple orientations. In this article, a robust method has been proposed for scene text detection having multiple orientations. The text in the images are horizontally, non-horizontally and curve oriented. The proposed method contains two main stages—maximally stable extremal region (MSER) computation and stroke width transformation (SWT). MSERs have been used to detect maximally stable extremal regions in the images. It is followed by implementing Canny edge detector for enhancement of edges in the images. To remove the non-text regions in the images, the combination of SWT image and geometric information has been used. The performance of the proposed method has been assessed on the IITR text datasets and it produces very encouraging results.