Arbitrary Reading Order Scene Text Spotter with Local Semantics Guidance
Jiahao Lyu, Wei Wang, Dongbao Yang, Jinwen Zhong, Yu Zhou

TL;DR
This paper introduces LSGSpotter, a novel scene text spotting method that guides recognition with local semantics, enabling arbitrary reading order detection and achieving state-of-the-art results on multiple benchmarks.
Contribution
The paper proposes a new scene text spotter that uses local semantic guidance and auto-regressive decoding to handle arbitrary reading orders without complex detection modules.
Findings
Achieves state-of-the-art performance on InverseText benchmark.
Improves accuracy on Total-Text and SCUT-CTW1500 datasets.
Effectively handles arbitrary-shaped scene texts in various reading orders.
Abstract
Scene text spotting has attracted the enthusiasm of relative researchers in recent years. Most existing scene text spotters follow the detection-then-recognition paradigm, where the vanilla detection module hardly determines the reading order and leads to failure recognition. After rethinking the auto-regressive scene text recognition method, we find that a well-trained recognizer can implicitly perceive the local semantics of all characters in a complete word or a sentence without a character-level detection module. Local semantic knowledge not only includes text content but also spatial information in the right reading order. Motivated by the above analysis, we propose the Local Semantics Guided scene text Spotter (LSGSpotter), which auto-regressively decodes the position and content of characters guided by the local semantics. Specifically, two effective modules are proposed in…
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Taxonomy
MethodsSoftmax · Attention Is All You Need
