Semantic Segmentation Using Super Resolution Technique as Pre-Processing

TL;DR

This paper explores the use of image super resolution as a preprocessing step to improve the accuracy and performance of semantic segmentation in document image binarization tasks.

Contribution

It introduces a novel approach of integrating super resolution techniques into semantic segmentation workflows for document images.

Findings

Super resolution preprocessing enhances segmentation accuracy.

Improved performance in document image binarization.

Effective integration of super resolution with semantic segmentation.

Abstract

Combining high-level and low-level visual tasks is a common technique in the field of computer vision. This work integrates the technique of image super resolution to semantic segmentation for document image binarization. It demonstrates that using image super-resolution as a preprocessing step can effectively enhance the results and performance of semantic segmentation.