Consecutive Convolutional Activations for Scene Character Recognition

Driven by the rapid growth of communication technologies and the wide applications of intelligent mobile terminals, the scene character recognition has become a significant yet very challenging task in people's lives. In this paper, we design a novel feature representation scheme termed consecutive convolutional activations (CCA) for character recognition in natural scenes. The proposed CCA could integrate both the low-level and the high-level patterns into the global decision by learning character representations from several successive convolutional layers. Concretely, one shallow convolutional layer is first selected for extracting the convolutional activation features, and then, the next consecutive deep convolutional layers are utilized to learn weight matrices for these convolutional activation features. Finally, the Fisher vectors are employed to encode the CCA features so as to obtain the image-level representations. Extensive experiments are conducted on two English scene character databases (ICDAR2003 and Chars74K) and one Chinese scene character database (“Pan+ChiPhoto”), and the experimental data indicate that the proposed method achieves a superior performance than the previous algorithms.