Work

HFI-Former: High-Frequency Interaction Transformer for Robust Scene Text Detection

Year: 2026

Type: article

Abstract: Scene text detection aims to accurately localize text instances in images captured under complex environments. Its performance depends heavily on precise text boundary delineation and reliable semantic discrimination from cluttered backgrounds. However, existing methods still struggle in such complex scenes. Repeated downsampling gradually biases features toward low-frequency components, thereby weakening edge details and local structures that are critical to text morphology. Additionally, semantic information and local details are often modeled independently. This lack of coordination makes high-frequency responses vulnerable to background noise. To address these issues, we propose HFI-Former, a Transformer-based model designed for high-frequency enhancement and feature interaction. The framework consists of multi-scale feature extraction, frequency-enhanced representation, semantic-guided feature interaction, and deformable Transformer encoding. Frequency-domain enhancement is introduced to preserve high-frequency structural features degraded by repeated downsampling. Semantic-aware feature interaction further injects global context to regulate multi-scale feature fusion. Experiments on CTW1500, Total-Text and ICDAR1500 demonstrate competitive boundary localization accuracy and strong overall detection performance in complex scenes.

Source: Information

DOI: 10.3390/info17040365

Authors: Yubing Gao, Quanli Gao, Lianhe Shao, Xihan Wang, Lufang Liu

Institution: Xi'an Polytechnic University

Language: English

Topic: Handwritten Text Recognition Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,930

Funder: National Natural Science Foundation of China