Industrial Scene Text Detection with Refined Feature-attentive Network

TL;DR

This paper introduces a refined feature-attentive network (RFN) that improves industrial scene text detection by enhancing localization accuracy through multi-resolution feature integration and attention mechanisms, validated on new and public datasets.

Contribution

The paper presents a novel RFN architecture with parallel feature integration and attention refinement, specifically designed for challenging industrial scene text detection.

Findings

Achieves state-of-the-art performance on industrial and public datasets.

Effectively improves localization accuracy in low-contrast, cluttered scenes.

Constructs large-scale industrial scene text datasets for training and evaluation.

Abstract

Detecting the marking characters of industrial metal parts remains challenging due to low visual contrast, uneven illumination, corroded character structures, and cluttered background of metal part images. Affected by these factors, bounding boxes generated by most existing methods locate low-contrast text areas inaccurately. In this paper, we propose a refined feature-attentive network (RFN) to solve the inaccurate localization problem. Specifically, we design a parallel feature integration mechanism to construct an adaptive feature representation from multi-resolution features, which enhances the perception of multi-scale texts at each scale-specific level to generate a high-quality attention map. Then, an attentive refinement network is developed by the attention map to rectify the location deviation of candidate boxes. In addition, a re-scoring mechanism is designed to select text boxes with the best rectified location. Moreover, we construct two industrial scene text datasets, including a total of 102156 images and 1948809 text instances with various character structures and metal parts. Extensive experiments on our dataset and four public datasets demonstrate that our proposed method achieves the state-of-the-art performance.