Natural scene text detection is an important task for image analysis and understanding.In this paper

a natural scene text detection method is proposed

using adaptive color clustering and context information analysis.Firstly

combining hierarchical clustering with self-learning strategy

we design an adaptive color clustering method

which learns clustering weights automatically and generates high character recall.Then

considering text in images usually containing several characters

we propose a character verification strategy based on image context information

which can guarantee high character recall and remove non-text components at the same time.Finally

characters are merged to text lines

and further post-processing is applied to generate final text detection results.Experiments on the ICDAR2013 publicly available dataset show that we obtain recall of 74.17%

precision of 83.40% and F-score of 78.52%.Compared with other text detection methods

our method obtains better text detection performance

indicating superiority of the proposed method.