Binary Document Image Super Resolution for Improved Readability and OCR Performance

TL;DR

This paper introduces deep neural network models for super-resolving low-resolution binary document images, specifically Tamil scripts, to enhance readability and OCR accuracy, using novel architectures and post-processing techniques.

Contribution

The paper presents new deep learning architectures for binary document image super-resolution tailored for Tamil scripts, improving OCR performance and readability.

Findings

Models significantly improve OCR accuracy on low-resolution images.

Post-processing enhances character connectivity and visual quality.

Deep learning approaches outperform traditional methods.

Abstract

There is a need for information retrieval from large collections of low-resolution (LR) binary document images, which can be found in digital libraries across the world, where the high-resolution (HR) counterpart is not available. This gives rise to the problem of binary document image super-resolution (BDISR). The objective of this paper is to address the interesting and challenging problem of super resolution of binary Tamil document images for improved readability and better optical character recognition (OCR). We propose multiple deep neural network architectures to address this problem and analyze their performance. The proposed models are all single image super-resolution techniques, which learn a generalized spatial correspondence between the LR and HR binary document images. We employ convolutional layers for feature extraction followed by transposed convolution and sub-pixel convolution layers for upscaling the features. Since the outputs of the neural networks are gray scale, we utilize the advantage of power law transformation as a post-processing technique to improve the character level pixel connectivity. The performance of our models is evaluated by comparing the OCR accuracies and the mean opinion scores given by human evaluators on LR images and the corresponding model-generated HR images.