Robust Text Detection in Natural Scene Images by Generalized Color-Enhanced Contrasting Extremal Region and Neural Networks

This paper presents a robust text detection approach based on generalized color-enhanced contrasting extremal region (CER) and neural networks. Given a color natural scene image, six component-trees are built from its gray scale image, hue and saturation channel images in a perception-based illumination invariant color space, and their inverted images, respectively. From each component-tree, generalized color-enhanced CERs are extracted as character candidates. By using a "divide-and-conquer" strategy, each candidate image patch is labeled reliably by rules as one of five types, namely, Long, Thin, Fill, Square-large and Square-small, and classified as text or non-text by a corresponding neural network, which is trained by an ambiguity-free learning strategy. After pruning non-text components, repeating components in each component-tree are pruned by using color and area information to obtain a component graph, from which candidate text-lines are formed and verified by another set of neural networks. Finally, results from six component-trees are combined, and a post-processing step is used to recover lost characters and split text lines into words as appropriate. Our proposed method achieves 85.72% recall, 87.03% precision, and 86.37% F-score on ICDAR-2013 "Reading Text in Scene Images" test set.