Hierarchical Text Detection: From Word Level to Character Level

Text detection is a challenging task in computer vision. In this paper, we focus on English text detection in a natural scene image. We propose a hierarchical approach for text detection, which unifies the word-level text detection and character-level detection as well as the text spatial layout. In our approach, we firstly use stroke width transformation (SWT) to filter an image in a word level. Secondly, we employ the random forest to select discriminative features of characters and compute the confident values of characters. Finally, we use conditional random field to integrate the discriminative information with the text spatial layout, which separates the text from the background. The proposed approach is implemented on the ICDAR dataset, which is a challenging dataset for text detection, and the experiment results demonstrate that our approach is efficient and effective, and it is superior to the state-of-the-art methods in comprehensive criteria.