In the big data era, the impetus to digitize the vast reservoirs of data trapped in unstructured scanned documents such as invoices, bank documents and courier receipts has gained fresh momentum. The scanning process often results in the introduction of artifacts such as background noise, blur due to camera motion, watermarkings, coffee stains, or faded text. These artifacts pose many readability challenges to current text recognition algorithms and significantly degrade their performance. Existing learning based denoising techniques require a dataset comprising of noisy documents paired with cleaned versions. In such scenarios, a model can be trained to generate clean documents from noisy versions. However, very often in the real world such a paired dataset is not available, and all we have for training our denoising model are unpaired sets of noisy and clean images. This paper explores the use of GANs to generate denoised versions of the noisy documents. In particular, where paired information is available, we formulate the problem as an image-to-image translation task i.e, translating a document from noisy domain ( i.e., background noise, blurred, faded, watermarked ) to a target clean document using Generative Adversarial Networks (GAN). However, in the absence of paired images for training, we employed CycleGAN which is known to learn a mapping between the distributions of the noisy images to the denoised images using unpaired data to achieve image-to-image translation for cleaning the noisy documents. We compare the performance of CycleGAN for document cleaning tasks using unpaired images with a Conditional GAN trained on paired data from the same dataset. Experiments were performed on a public document dataset on which different types of noise were artificially induced, results demonstrate that CycleGAN learns a more robust mapping from the space of noisy to clean documents.
This paper introduces a technique to remove visible watermark automatically using image inpainting algorithms. The pending images which need watermark re-moval are assumed to have same resolution and watermark region and we will show this assumption is reasonable. Our proposed technique includes two basic step. The first step is detecting the watermark region, we propose a statistical method to detect the watermark region. Thresholding algorithm for segmentation proceeds at the accumulation image which is calculated by accumulation of the gray-scale maps of pending images. The second step is removing the watermark using image inpainting algorithms. Since watermarks are usually with large re-gion areas, an exemplar-based inpainting algorithm through investigating the sparsity of natural image patches is proposed for this step. Experiments were im-plemented in a test image set of 889 images downloaded from a shopping web-site with the resolution of 800∗800 and same watermark regions. Abstract In this paper, we propose two schemes for visible-watermark removal and reversible image recovery. In the first scheme, we consider the scenario for the image generated by a specific visible (not completely reversible) watermarking algorithm (2017). A run-length coding based method is utilized to compress the difference between the preliminary recovered image and original image. After embedding the difference information invisibly and reversibly, the final embedded image can be exactly recovered to its original version after visible-watermark removal, which avoids the problem of overflow and underflow in (2017). In the second scheme, the scenario of visible-watermark removal for the image generated by any visible watermarking algorithms (no matter the sender and the receiver know the algorithms or not) is considered. The scheme can perfectly remove the embedded visible watermark and can also exactly recover original image with the assist of image inpainting technique. In addition, for both two proposed schemes, the invalid user without the knowledge of secret key cannot achieve reversible recovery for original image. Experimental results demonstrate the effectiveness and superiority of our schemes.