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Document Rectification and Illumination Correction using a Patch-based CNN | Papers With Code
Source: paperswithcode.com
Article summary:
1. This article proposes a novel learning method to rectify document images with various distortion types from a single input image.
2. The proposed approach seeks to first learn the distortion flow on input image patches rather than the entire image.
3. A second network is proposed to correct the uneven illumination, further improving the readability and OCR accuracy.
Article analysis:
The article is generally trustworthy and reliable, as it provides an overview of a novel learning method for document rectification and illumination correction using a patch-based CNN. The article is well-structured and provides clear explanations of the proposed approach, as well as results from experiments conducted on both synthetic and real datasets.
The article does not appear to be biased or one-sided in its reporting, as it presents both sides of the argument equally. It also does not contain any unsupported claims or missing points of consideration, as all claims are backed up by evidence from experiments conducted on both synthetic and real datasets.
The article does not contain any promotional content or partiality, nor does it present any possible risks associated with the proposed approach that could potentially affect its accuracy or reliability. Furthermore, all counterarguments are explored in detail throughout the article, providing readers with a comprehensive understanding of the proposed approach and its potential implications for document rectification and illumination correction tasks.