Text detection approach based on confidence map and context information

Abstract Text information plays a significant role in many applications for providing more descriptive and abstract information than other objects. In this paper, an approach based on the confidence map and context information is proposed to robustly detect texts in natural scenes. Most of the conventional methods design sophisticated texture features to describe the text regions, while we focus on building a confidence map model by integrating the seed candidate appearance and the relationships with its adjacent candidates to highlight the texts from the backgrounds, and the candidates with low confidence value will be removed. In order to improve the recall rate, the text context information is adopted to regain the missing text regions. Finally, the text lines are formed and further verified, and the words are obtained by calculating the threshold to separate the intra-word letters from the inter-word letters. Experimental results on the three public benchmark datasets, i.e., ICDAR 2005, ICDAR 2011 and ICDAR 2013, show that the proposed approach has achieved the competitive performances by comparing with the other state-of-the-art methods.