Text detection in images is an important prerequisite for many image content analysis tasks. Actually, nearly all the widely-used methods focus on English and Chinese text detection while some minority language, such as Uyghur language, text detection is paid less attention by researchers. In this paper, we propose a system which detects Uyghur language text in images. First, component candidates are detected by channel-enhanced Maximally Stable Extremal Regions (MSERs) algorithm. Then, most non-text regions are removed by a two-layer filtering mechanism. Next, the rest component regions are connected into short chains, and the short chains are connected into complete chains. Finally, the non-text chains are pruned by a chain elimination filter. To evaluate our algorithm, we generate a new dataset by various Uyghur texts. As a result, 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 83.5%, much better than the state-of-the- art performance of 75.5%.