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 $32times128$), 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 $8times$ faster and $96%$ smaller than it full-precision version.
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