[2209.00224] 1st Place Solution to ECCV 2022 Challenge on Out of Vocabulary Scene Text Understanding: End-to-End Recognition of Out of Vocabulary Words
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arXiv:2209.00224 (cs)
[Submitted on 1 Sep 2022]

Title:1st Place Solution to ECCV 2022 Challenge on Out of Vocabulary Scene Text Understanding: End-to-End Recognition of Out of Vocabulary Words

Authors:Zhangzi Zhu, Chuhui Xue, Yu Hao, Wenqing Zhang, Song Bai
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Abstract:Scene text recognition has attracted increasing interest in recent years due to its wide range of applications in multilingual translation, autonomous driving, etc. In this report, we describe our solution to the Out of Vocabulary Scene Text Understanding (OOV-ST) Challenge, which aims to extract out-of-vocabulary (OOV) words from natural scene images. Our oCLIP-based model achieves 28.59\% in h-mean which ranks 1st in end-to-end OOV word recognition track of OOV Challenge in ECCV2022 TiE Workshop.
Comments: Report to ECCV TiE OOV competition
Subjects: Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2209.00224 [cs.CV]
  (or arXiv:2209.00224v1 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2209.00224

Submission history

From: Chuhui Xue [view email]
[v1] Thu, 1 Sep 2022 04:53:13 UTC (17 KB)
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