Harit, Gaurav

We propose a graph-based deep network for predicting the associations pertaining to field labels and field values in heterogeneous handwritten form images. We consider forms in which the field label comprises printed text and field value can be the handwritten text. Inspired by the relationship predicting capability of the graphical models, we use a Graph Autoencoder to perform the intended field label to field value association in a given form image. To the best of our knowledge, it is the first attempt to perform label-value association in a handwritten form image using a machine learning approach. We have prepared our handwritten form image dataset comprising 300 images from 30 different templates having 10 images per template. Our framework is experimented on different network parameter and has shown promising results.

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.