Harit, Gaurav

Automated identification of mathematical expressions (MEs) is essential in transforming scientific and engineering documents into electronic form. Even though character and symbol recognizers have achieved commendable performance for digitizing documents, structure analysers still face a challenge in correctly interpreting the maths expressions. This review paper compares the salient aspects of past works dealing with structure analysis of printed and handwritten MEs. To the best of our knowledge, no previous work has done a systematic study of structural analysis methods in mathematical expression recognition. We present distinguishing aspects of different grammars and their production rules for semantic parsing of ME. Our study contributes by providing information on the existing datasets, their desirable properties, different evaluation measures, distinguishing aspects of techniques used and future research directions in structural analysis.

In this paper, we propose a unified approach for denoising and super-resolution of document images. The approach is a one shot unpaired technique where a single unpaired example is used as reference for training a SinGAN (Shaham et al., in: Proceedings of the IEEE/CVF international conference on computer vision, 2019) model. The training is carried out in 2 steps. First we use a clean reference image to train a SinGAN to learn the characteristics of the clean image. Then we perform super resolution and denoising of given test image using another SinGAN. Our unique formulation of the loss function helps in this task by prompting the generated images to have characteristics similar to the reference clean image. We conduct experiments on publicly available datasets (Kaggle Dirty Documents Images and DIBCO) and obtain promising results. We also evaluate the performance of our model for OCR and obtain a higher recognition rate compared to competing methods.

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.