  Document shadow removal is an integral task in document enhancement
pipelines, as it improves visibility, readability and thus the overall quality.
Assuming that the majority of practical document shadow removal scenarios
require real-time, accurate models that can produce high-resolution outputs
in-the-wild, we propose Laplacian Pyramid with Input/Output Attention Network
(LP-IOANet), a novel pipeline with a lightweight architecture and an upsampling
module. Furthermore, we propose three new datasets which cover a wide range of
lighting conditions, images, shadow shapes and viewpoints. Our results show
that we outperform the state-of-the-art by a 35% relative improvement in mean
average error (MAE), while running real-time in four times the resolution (of
the state-of-the-art method) on a mobile device.
