Text detection in images based on unsupervised classification of edge-based features

In this paper, an algorithm is proposed for detecting texts in images and video frames. It is performed by three steps: edge detection, text candidate detection and text refinement detection. Firstly, it applies edge detection to get four edge maps in horizontal, vertical, up-right, and up-left direction. Secondly, the feature is extracted from four edge maps to represent the texture property of text. Then k-means algorithm is applied to detect the initial text candidates. Finally, the text areas are identified by the empirical rules analysis and refined through project profile analysis. Experimental results demonstrate that the proposed approach could efficiently be used as an automatic text detection system, which is robust for font size, font color, background complexity and language.