Core Concepts
Utilizing Pre-trained Language Models enhances scene text spotting by reducing the reliance on precise detection and improving recognition accuracy.
Abstract
The article discusses a novel approach to scene text spotting using Pre-trained Language Models (PLMs) without the need for precise detection. The proposed method leverages advanced PLMs to enhance performance without fine-grained detection, achieving accurate recognition. By combining block-level text detection with PLM-based recognition, the system effectively handles complex scenarios like multi-line, reversed, occluded, and incomplete-detection texts. Extensive experiments demonstrate superior performance across multiple public benchmarks. The study also explores the potential of entirely detection-free spotting using PLMs.
Stats
Inspired by the glimpse-focus spotting pipeline of human beings.
Proposed scene text spotter leverages advanced PLMs.
Achieved accurate recognition through block-level text detection.
Demonstrated superior performance across multiple public benchmarks.
Quotes
"Can machines spot texts without precise detection just like human beings?"
"Is text block another alternative for scene text spotting other than word or character?"
"Our PLM-powered recognizer achieves higher accuracy in processing complex situations compared to previous methods."