mlsanityData to 7 Oct 2026

Text Detection & Recognition in the Wild for Robot Localization

arXiv 2205.08565 ↗First posted 17 May 2022cs.CV cs.ROcited by 2

Not on the map. Why?

Abstract (arXiv)

Signage is everywhere and a robot should be able to take advantage of signs to help it localize (including Visual Place Recognition (VPR)) and map. Robust text detection & recognition in the wild is challenging due to such factors as pose, irregular text, illumination, and occlusion. We propose an end-to-end scene text spotting model that simultaneously outputs the text string and bounding boxes. This model is more suitable for VPR. Our central contribution is introducing utilizing an end-to-end scene text spotting framework to adequately capture the irregular and occluded text regions in different challenging places. To evaluate our proposed architecture's performance for VPR, we conducted several experiments on the challenging Self-Collected Text Place (SCTP) benchmark dataset. The initial experimental results show that the proposed method outperforms the SOTA methods in terms of precision and recall when tested on this benchmark.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 42 of its 73 references that are in the map. First posted on arXiv 17 May 2022.

Builds on

42 of 73 references.

References · 42

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30 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Leveraging Semantic Maps for City-Scale Cross-View Localizationcited by 0
  2. 2026Back-Tracking from Clarity: Self-Learning to See Text from Afarcited by 0