A Novel Algorithm for Text Detection and Localization in Natural Scene Images

Text data in an image present useful information for annotation, indexing and structuring of images. The gathered information from images can be applied for devices for impaired people, navigation, tourist assistance or georeferencing business. In this paper we propose a novel algorithm for text detection and localization from outdoor/indoor images which is robust against different font size, style, uneven illumination, shadows, highlights, over exposed regions, low contrasted images, specular reflections and many distortions which makes text localization task harder. A binarization algorithm based on difference of gamma correction and morphological reconstruction is realized to extract the connected components of an image. These connected components are classified as text and non test using a Random Forest classifier. After that text regions are localized by a novel merging algorithm for further processing.