LRANet++: Low-Rank Approximation Network for Accurate and Efficient Text Spotting

TL;DR

LRANet++ introduces a low-rank shape representation and a triple assignment detection scheme to improve the accuracy and efficiency of end-to-end arbitrary-shaped text spotting.

Contribution

It proposes a novel low-rank approximation method for precise shape detection and a triple assignment detection head for faster inference in text spotting.

Findings

Outperforms state-of-the-art methods on challenging benchmarks.

Achieves high accuracy with reduced inference time.

Effectively handles arbitrary-shaped text detection and recognition.

Abstract

End-to-end text spotting aims to jointly optimize text detection and recognition within a unified framework. Despite significant progress, designing an accurate and efficient end-to-end text spotter for arbitrary-shaped text remains challenging. We identify the primary bottleneck as the lack of a reliable and efficient text detection method. To address this, we propose a novel parameterized text shape representation based on low-rank approximation for precise detection and a triple assignment detection head for fast inference. Specifically, unlike current data-irrelevant shape representation methods, we exploit shape correlations among labeled text boundaries to construct a robust low-rank subspace. By minimizing an $\ell_1$-norm objective, we extract orthogonal vectors that capture the intrinsic text shape from noisy annotations, enabling precise reconstruction via the linear combination of only a few basis vectors. Next, the triple assignment scheme decouples training complexity from inference speed. It utilizes a deep sparse branch to guide an ultra-lightweight inference branch, while a dense branch provides rich parallel supervision. Building upon these advancements, we integrate the enhanced detection module with a lightweight recognition branch to form an end-to-end text spotting framework, termed LRANet++, capable of accurately and efficiently spotting arbitrary-shaped text. Extensive experiments on challenging benchmarks demonstrate the superiority of LRANet++ compared to state-of-the-art methods. Code is available at: https://github.com/ychensu/LRANet-PP.