IFR: Iterative Fusion Based Recognizer For Low Quality Scene Text Recognition

TL;DR

This paper introduces IFR, an iterative fusion recognizer that enhances low-quality scene text recognition by combining image restoration and recognition in a collaborative framework, significantly improving accuracy.

Contribution

The paper proposes a novel iterative fusion framework with a feature fusion module for low-quality scene text recognition, without altering existing recognition network structures.

Findings

Significant accuracy improvements on benchmark datasets.

Effective handling of low-resolution and degraded images.

Outperforms baseline methods in recognition tasks.

Abstract

Although recent works based on deep learning have made progress in improving recognition accuracy on scene text recognition, how to handle low-quality text images in end-to-end deep networks remains a research challenge. In this paper, we propose an Iterative Fusion based Recognizer (IFR) for low quality scene text recognition, taking advantage of refined text images input and robust feature representation. IFR contains two branches which focus on scene text recognition and low quality scene text image recovery respectively. We utilize an iterative collaboration between two branches, which can effectively alleviate the impact of low quality input. A feature fusion module is proposed to strengthen the feature representation of the two branches, where the features from the Recognizer are Fused with image Restoration branch, referred to as RRF. Without changing the recognition network structure, extensive quantitative and qualitative experimental results show that the proposed method significantly outperforms the baseline methods in boosting the recognition accuracy of benchmark datasets and low resolution images in TextZoom dataset.