A bilingual text detection in natural images using heuristic and unsupervised learning

Digital images are being produced in a massive number every day. Acomponent that may exist in digital images is text. Textual information can beextracted and used in a variety of fields. Noise, blur, distortions, occlusion, fontvariation, alignments, and orientation, are among the main challenges for textdetection in natural images. Despite many advances in text detection algorithms,there is not yet a single algorithm that addresses all of the above problemssuccessfully. Furthermore, most of the proposed algorithms can only detecthorizontal texts and a very small fraction of them consider Farsi language. Inthis paper, a method is proposed for detecting multi-orientated texts in both Farsiand English languages. We have defined seven geometric features to distinguishtext components from the background and proposed a new contrast enhancementmethod for text detection algorithms. Our experimental results indicate that theproposed method achieves a high performance in text detection on natural images.

Keywords:

Text Detection , Natural Images , Mean Shift Clustering , bilingual Text