@inproceedings {2016,
	title = {A Binary Convolutional Encoder-decoder Network for Real-time Natural Scene Text Processing},
	year = {2016},
	month = {12/2016},
	abstract = {<p>In this paper, we develop a binary convolutional encoder-decoder network (B-CEDNet) for natural scene text processing (NSTP). It converts a text image to a class-distinguished salience map that reveals the categorical, spatial and morphological information of characters. The existing solutions are either memory consuming or run-time consuming that cannot be applied to real-time applications on resource-constrained devices such as advanced driver assistance systems. The developed network can process multiple regions containing characters by one-off forward operation, and is trained to have binary weights and binary feature maps, which lead to both remarkable inference run-time speedup and memory usage reduction. By training with over 200, 000 synthesis scene text images (size of 32 \&times; 128), it can achieve 90\% and 91\% pixel-wise accuracy on ICDAR-03 and ICDAR-13 datasets. It only consumes 4.59 ms inference run-time realized on GPU with a small network size of 2.14 MB, which is up to 8\&times; faster and 96\% smaller than it full-precision version.\&nbsp;</p>
},
	keywords = {psclab},
	author = {Liu, Zichuan and Yixing Li and Ren, Fengbo and Yu, Hao}
}
