Harit, Gaurav

Image denoising deals with removal of unwanted noise from images. While there have been many techniques that can be applied to denoise a given input noisy image, the methods process an image in its entirety, assuming that the noise uniformly affects the entire image. For inputs where the noise affects a localised part of the image, applying methods that attempt to denoise the entire image can adversely affect the clean portions. To address this problem, we propose a deep reinforcement learning-based framework aiming to overcome this limitation and achieve better results for images with non-uniformly distributed noise. We propose a two-step procedure that first identifies the noisy patch and then denoises the extracted patch. We use a reinforcement learning-based approach for noise localization and use PixelRL for noise removal. We have prepared a comprehensive dataset specifically for the noise localization problem, and noise patches are induced in clean document images using various noise patterns, such as Gaussian noise, coffee stains, and ink bleeds.

In this paper we propose a thinning methodology applicable to character images. It is novel in terms of its ability to adapt to local character shape while constructing the thinned skeleton. Our method does not produce many of the distortions in the character shapes which normally result from the use of existing thinning algorithms. The proposed thinning methodology is based on the medial axis of the character. The skeleton has a width of one pixel. As a by-product of our thinning approach, the skeleton also gets segmented into strokes in vector form. Hence further stroke segmentation is not required. We have conducted experiments with printed and handwritten characters in several scripts such as English, Bengali, Hindi, Kannada and Tamil. We obtain less spurious branches compared to other thinning methods. Our method does not use any kind of post processing.

Digital skeleton of character images, generated by thinning method, has a wide range of applications for shape analysis and classification. But thinning of character images is a big challenge. Removal of spurious strokes or deformities in thinning is a difficult problem. In this paper, we propose a contour-based thinning method used for performing skeletonization of printed noisy isolated character images. In this method, we use shape characteristics of text to get skeleton of nearly same as the true character shape. This approach helps to preserve the local features and true shapes of the character images. As a by-product of our thinning approach, the skeleton also gets segmented into strokes in vector form. Hence further stroke segmentation is not required. Experiment is done on printed English, Bengali, Hindi, and Tamil characters and we obtain much better results comparing with other thinning methods without any post-processing.

In this paper, we present novel topological features based on the structural shape of a character. We detect the convexshaped segments formed by the various strokes. The convex segments are then represented with shape primitives from a repertoire. The character is represented as a spatial layout of convex segments. We formulate feature templates for Bangla characters. A given character is assigned the label of the best matching feature template. We have tested the method on a benchmark datasets of printed and handwritten Bangla basic and compound character images. Our results demonstrate the efficacy of our approach. Copyright © 2011 ACM.