arXiv:1604.04018cs.CV
Multi-Oriented Text Detection with Fully Convolutional Networks
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Abstract
In this paper, we propose a novel approach for text detec- tion in natural images. Both local and global cues are taken into account for localizing text lines in a coarse-to-fine pro- cedure. First, a Fully Convolutional Network (FCN) model is trained to predict the salient map of text regions in a holistic manner. Then, text line hypotheses are estimated by combining the salient map and character components. Fi- nally, another FCN classifier is used to predict the centroid of each character, in order to remove the false hypotheses. The framework is general for handling text in multiple ori- entations, languages and fonts. The proposed method con- sistently achieves the state-of-the-art performance on three text detection benchmarks: MSRA-TD500, ICDAR2015 and ICDAR2013.
E.g [1] trained for 20 * 10^5 iterations at 2.1 seconds per image, coming out to 48 days (!) in total. Maybe the 2.1 seconds also include the post-processing to get bounding boxes, though. Anyway, you need lots of training time. [1] https://arxiv.org/abs/1604.04018…
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