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. 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. 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. The proposed text detection method is robust for natural scene images

and experimental results show the effectiveness of the proposed method.