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In this paper, we develop a binary convolutional encoder-decoder network(B-CEDNet) for natural scene text processing (NSTP). It converts a text imageto a class-distinguished salience map that reveals the categorical, spatial andmorphological information of characters. The existing solutions are eithermemory consuming or run-time consuming that cannot be applied to real-timeapplications on resource-constrained devices such as advanced driver assistancesystems. The developed network can process multiple regions containingcharacters by one-off forward operation, and is trained to have binary weightsand binary feature maps, which lead to both remarkable inference run-timespeedup and memory usage reduction. By training with over 200, 000 synthesisscene text images (size of $32\times128$), 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-precisionversion.

A Binary Convolutional Encoder-decoder Network for Real-time Natural Scene Text Processing