A Bidirectional Information Aggregation Architecture for Scene Text Detection

TextBoxes[1] is one of the most advanced text detection method in both aspects of accuracy and efficiency, but it is still not very sensitive to the small text in natural scenes and often can not localize text regions precisely. To tackle these problems, we first present a Bidirectional Information Aggregation (BIA) architecture by effectively aggregating multi-scale feature maps to enhance local details and strengthen context information, making the detector not only work reliably on multi-scale text, especially the small text, but also predict more precise boxes for texts. This architecture also results in a single classifier network, which allows our model to be trained much faster and easily with better generalization power. Then, we propose to use multiple symmetrical feature maps for feature extraction in the test stages for further improving the performance on the small text. To further promote precise predicting boxes, we present a statistical grouping method that operates on the training set bounding boxes to generate aspect ratios for default boxes. Finally, our model not only outperforms the TextBoxes without much time overhead, but also provides promising performance compared to the recent state-of-theart methods on the ICDAR 2011 and 2013 database.