Text Detection & Recognition in the Wild for Robot Localization
- URL: http://arxiv.org/abs/2205.08565v2
- Date: Thu, 19 May 2022 14:04:10 GMT
- Title: Text Detection & Recognition in the Wild for Robot Localization
- Authors: Zobeir Raisi and John Zelek
- Abstract summary: We propose an end-to-end scene text spotting model that simultaneously outputs the text string and bounding boxes.
Our central contribution is introducing utilizing an end-to-end scene text spotting framework to adequately capture the irregular and occluded text regions.
- Score: 1.52292571922932
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: 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.
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