目的 目前，基于MSERs(maximally stable extremal regions)的文本检测方法是自然场景图像文本检测的主流方法。但是自然场景图像中部分文本的背景复杂多变，MSERs算法无法将其准确提取出来，降低了该类方法的鲁棒性。本文针对自然场景图像文本背景复杂多变的特点，将MSCRs(maximally stable color regions)算法用于自然场景文本检测，提出一种结合MSCRs与MSERs的自然场景文本检测方法。方法 首先采用MSCRs算法与MSERs算法提取候选字符区域；然后利用候选字符区域的纹理特征训练随机森林字符分类器，对候选字符区域进行分类，从而得到字符区域；最后，依据字符区域的彩色一致性和几何邻接关系对字符进行合并，得到最终文本检测结果。结果 本文方法在ICDAR 2013上的召回率、准确率和F值分别为71.9%、84.1%和77.5%，相对于其他方法的召回率和F值均有所提高。结论 本文方法对自然场景图像文本检测具有较强的鲁棒性，实验结果验证了本文方法的有效性。

Objective Text detection methods based on the maximally stable extremal regions (MSERs)algorithm are now widely used in natural scene text detection. However, text regions in natural scene images can have complex backgrounds that differ from those in documents and business cards, which cannot be accurately extracted by the MSERs algorithm. A text detection method is proposed for natural scene images by integrating the maximally stable color regions (MSCRs)into MSERs in this study to overcome the said problem. Method The character candidates are first extracted with both the MSCRs and MSERs algorithms. Parts of the non-character candidates are then eliminated according to the geometric information. The texture features are exploited to distinguish the character and non-character candidates, and a random forest character classifier is trained. The non-character candidates are then eliminated according to the classification result of the character classifier. Finally, the single character candidates are grouped into text regions according to the color similarity and geometric adjacency information. Result The proposed natural scene text detection method achieved 71.9%, 84.1%, and 77.5% in recall rate, precision rate, and f-score on the ICDAR 2013 database, respectively. The recall rate and f-score improved, unlike other state-of-the-art methods. Conclusion The proposed text detection method is robust for natural scene images, and experimental results show the effectiveness of the proposed method.