Scene Text Detection via Edge Cue and Multi features
Inspired by the fact that edge is an important cue to distinguish texts from background, we propose a novel scene text detection method via edge cue and multiple features, which has two main parts, i.e. candidate character region (CCR) extraction and region classification. For CCR extraction, the edges are first extracted from the input image, which are then broken and merged based on color features to form the final edge image. For each edge connected component, a number of image patches are extracted by translating and scaling its boundary rectangle to generate the CCRs. For region classification, the character regions are extracted from the CCRs by using a region classification technique, which extracts both the hand designed low level features and deep convolution neural network based high level features of the regions for classification. And then the character regions are merged to form the candidate text regions, based on which the final text region are detected by using the region classification technique. The proposed method is evaluated on two latest ICDAR benchmark datasets and the experimental results demonstrate that the proposed method outperforms the state of the art approaches of scene text detection.
2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR)