Abstract
Binarization of document is considered the first key step in many document processing tasks. In this paper, we try to reformulate the problem as an image-to-image translation. Most of the existing learning methods for document binarization make use of supervised approach, but obtaining ground truth for binarized documents is difficult. Here we developed an unsupervised adversarial training procedure for binarization. We use disentangling of style and content from a binarized document and transfer the binarized style to the input document. Our results indicate that this approach works on par with many other results published in literature.
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Salman, K.H., Bhagvati, C. (2022). Unsupervised Document Binarization Via Disentangled Representation. In: Bhateja, V., Tang, J., Satapathy, S.C., Peer, P., Das, R. (eds) Evolution in Computational Intelligence. Smart Innovation, Systems and Technologies, vol 267. Springer, Singapore. https://doi.org/10.1007/978-981-16-6616-2_39
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DOI: https://doi.org/10.1007/978-981-16-6616-2_39
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