Text detection in natural and computer-generated images

Abstract

Text detection is one of the most challenging and commonly dealt applications in computer vision. Detecting text regions is the first step of the text recognition systems called Optical Character Recognition. This process requires the separation of text region from non-text region. In this paper, we utilize Maximally Stable Extremal Regions to acquire very first text region candidates. Then these possible regions are reduced in quantity by using geometric and stroke width properties. Candidate regions are joined to obtain text groups. Finally, Tesseract Optical Character Recognition engine is utilized as the last step to eliminate non-text groups. We evaluated the proposed system on KAIST and ICDAR datasets for both natural images and computer-generated images. For natural images 82.7% precision and 52.0% f-accuracy; for computer-generated images 64.0% precision and 65.2% f-accuracy is achieved.