Layer Separation via a Spatial-Attention GAN

Modern OCR systems are achieving excellent results on difficult, even physically degraded, documents. One persistent limitation is the inability to disentangle overlapping ink, from various sources like handwriting, stamps, watermarks, and printed ink. In this work, we develop an approach to layer separation of documents into distinct layers which contain dedicated ink for these respective components. Our work builds on recent progress in image decomposition and employs a spatial-attention bypass enabling us to overwrite and in-paint challenging overlap regions while still maintaining near perfect reconstruction for undisturbed regions of the documents. Compared to baseline approaches, our approach improves on both perceptual metrics on the layer reconstruction task and end-to-end character error rate (CER) measured on an OCR system.