  The identification and restoration of ancient watermarks have long been a
major topic in codicology and history. Classifying historical documents based
on watermarks is challenging due to their diversity, noisy samples, multiple
representation modes, and minor distinctions between classes and intra-class
variations. This paper proposes a modified U-net-based conditional generative
adversarial network (GAN) named Npix2Cpix to translate noisy raw historical
watermarked images into clean, handwriting-free watermarked images by
performing image translation from degraded (noisy) pixels to clean pixels.
Using image-to-image translation and adversarial learning, the network creates
clutter-free images for watermark restoration and categorization. The generator
and discriminator of the proposed GAN are trained using two separate loss
functions, each based on the distance between images, to learn the mapping from
the input noisy image to the output clean image. After using the proposed GAN
to pre-process noisy watermarked images, Siamese-based one-shot learning is
employed for watermark classification. Experimental results on a large-scale
historical watermark dataset demonstrate that cleaning the noisy watermarked
images can help to achieve high one-shot classification accuracy. The
qualitative and quantitative evaluation of the retrieved watermarked image
highlights the effectiveness of the proposed approach.
