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1st Place Solution to ECCV 2022 Challenge on Out of Vocabulary Scene Text Understanding: End-to-End Recognition of Out of Vocabulary Words. (arXiv:2209.00224v1 [cs.CV])
Sept. 2, 2022, 1:14 a.m. | Zhangzi Zhu, Chuhui Xue, Yu Hao, Wenqing Zhang, Song Bai
cs.CV updates on arXiv.org arxiv.org
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.
More from arxiv.org / cs.CV updates on arXiv.org
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