Scene text localization using edge analysis and feature pool

Due to the rapid development of machine learning and data mining in nowadays, how to acquire information from images becomes more and more important. The direct information of an image is the texts inside. However, detecting such texts in images is always a challenging problem in computer vision area.Edge is one of the most important clues in scene character detection task. However, many edge based text detection methods usually had trouble with sticky edges and did not fully utilize characteristic of texts. In this paper, we proposed a method for detecting and localizing texts in natural scene images, by edge recombining, edge filtering and multi-channel processing. In order to segment texts from backgrounds, edges are firstly over-segmented into edge segments during edge analysis. These edge segments are then recombined to candidate characters and an edge filter is used to filter out most of background edges. The left candidate character edges are linked up to candidate text lines. We use two different classifiers to filter out non-text lines. To classify more accurately, extracted edge-based and region-based features are firstly stored in feature pools. Then we use liner SVM to select the most effective features from the feature pool to train classifiers. Finally, multi-channel is used to ensure the recall and a modified non-maximal suppress is applied to eliminate duplicate results. Experimental results on the ICDAR 2011 competition dataset and SVT database demonstrate the effectiveness of our method.