Recognizing text-based traffic guide panels with cascaded localization network

Abstract

In this paper, we introduce a new top-down framework for automatic localization and recognition of text-based traffic guide panels (http://tinyurl.com/wiki-guide-signs) captured by car-mounted cameras from natural scene images. The proposed framework involves two contributions. First, a novel Cascaded Localization Network (CLN) joining two customized convolutional nets is proposed to detect the guide panels and the scene text on them in a coarse-to-fine manner. In this network, the popular character-wise text saliency detection is replaced with string-wise text region detection, which avoids numerous bottomup processing steps such as character clustering and segmentation. Text information contained within detected text regions is then interpreted by a deep recurrent model without character segmentation required. Second, a temporal fusion of text region proposals across consecutive frames is introduced to significantly reduce the redundant computation in neighboring frames. A new challenging Traffic Guide Panel dataset is collected to train and evaluate the proposed framework, instead of the unsuited symbol-based traffic sign datasets. Experimental results demonstrate that our proposed framework outperforms multiple recently published text spotting frameworks in real highway scenarios.