Uyghur Language Text Detection in Complex Background Images Using Enhanced MSERs

Abstract

Text detection in complex background images is an important prerequisite for many image content analysis tasks. Actually, nearly all the widely-used methods of text detection focus on English and Chinese while some minority languages, such as Uyghur language, are paid less attention by researchers. In this paper, we propose a system which detects Uyghur language text in complex background images. First, component candidates are detected by the channel-enhanced Maximally Stable Extremal Regions (MSERs) algorithm. Then, most non-text regions are removed by a two-layer filtering mechanism. Next, the remaining component regions are connected into short chains, and the short chains are expanded by an expansion algorithm to connect the missed MSERs. Finally, the chains are identified by a Random Forest classifier. Experimental comparisons on the proposed dataset prove that our algorithm is effective for detecting Uyghur language text in complex background images. The F-measure is 84.8%, much better than the state-of-the-art performance of 75.5%.