UDoc-GAN: Unpaired Document Illumination Correction with Background Light Prior
Yonghui Wang, Wengang Zhou, Zhenbo Lu, Houqiang Li

TL;DR
UDoc-GAN is a novel unpaired framework that corrects uneven document illumination by predicting ambient light features and reformulating cycle consistency, improving text clarity without needing paired training data.
Contribution
It introduces the first unpaired document illumination correction method leveraging ambient light features and a re-formulated cycle consistency constraint.
Findings
Outperforms state-of-the-art in CER and ED metrics.
Demonstrates effective illumination correction on unpaired datasets.
Preserves textual details better than existing methods.
Abstract
Document images captured by mobile devices are usually degraded by uncontrollable illumination, which hampers the clarity of document content. Recently, a series of research efforts have been devoted to correcting the uneven document illumination. However, existing methods rarely consider the use of ambient light information, and usually rely on paired samples including degraded and the corrected ground-truth images which are not always accessible. To this end, we propose UDoc-GAN, the first framework to address the problem of document illumination correction under the unpaired setting. Specifically, we first predict the ambient light features of the document. Then, according to the characteristics of different level of ambient lights, we re-formulate the cycle consistency constraint to learn the underlying relationship between normal and abnormal illumination domains. To prove the…
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Taxonomy
TopicsHandwritten Text Recognition Techniques · Multimodal Machine Learning Applications · Video Analysis and Summarization
