Unsupervised Document Binarization of Engineering Drawings via Multi Noise CycleGAN
The task of document binarization of degraded complex documents is tremendously challenging due to the various forms of noise often present in these documents. While the current state-of-the-art deep learning approaches are capable for the removal of various noise types in documents with high accura...
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| Main Authors: | , , |
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| Format: | Article |
| Published: |
2023
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| Online Access: | http://scholars.utp.edu.my/id/eprint/37611/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-85168803705&doi=10.14569%2fIJACSA.2023.0140791&partnerID=40&md5=af01ea6cb9d491b43f810d3933911448 |
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