 |
    |
Robust Scene Text Detection with Convolution Neural Network Induced MSER Trees
Weilin Huang1, 2, Yu Qiao1, and Xiaoou Tang2, 1
1Shenzhen Key Lab of Comp. Vis and Pat. Rec., Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China
2Department of Information Engineering, The Chinese University of Hong Kong, China
Abstract. Maximally Stable Extremal Regions (MSERs) have achieved great success in scene text detection. However, this low-level pixel operation inherently limits its capability for handling complex text information efficiently (e. g. connections between text or background components), leading to the difficulty in distinguishing texts from background components. In this paper, we propose a novel framework to tackle this problem by leveraging the high capability of convolutional neural network (CNN). In contrast to recent methods using a set of low-level heuristic features, the CNN network is capable of learning high-level features to robustly identify text components from text-like outliers (e.g. bikes, windows, or leaves). Our approach takes advantages of both MSERs and sliding-window based methods. The MSERs operator dramatically reduces the number of windows scanned and enhances detection of the low-quality texts. While the sliding-window with CNN is applied to correctly separate the connections of multiple characters in components. The proposed system achieved strong robustness against a number of extreme text variations and serious real-world problems. It was evaluated on the ICDAR 2011 benchmark dataset, and achieved over 78% in F-measure, which is significantly higher than previous methods.
Keywords: Maximally Stable Extremal Regions (MSERs), convolutional neural network (CNN), text-like outliers, sliding-window
LNCS 8692, p. 497 ff.
lncs@springer.com
© Springer International Publishing Switzerland 2014