mlsanityData to 14 Oct 2026

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

arXiv 2209.00224 ↗First posted 1 Sep 2022cs.CVcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 15 of its 17 references that are in the map. First posted on arXiv 1 Sep 2022.

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References · 15

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  12. 2020SCATTER: Selective Context Attentional Scene Text Recognizercited by 69
3 more
  1. 2021Open Images V5 Text Annotation and Yet Another Mask Text Spottercited by 46
  2. 2022Language Matters: A Weakly Supervised Vision-Language Pre-training Approach for Scene Text Detection and Spottingcited by 27
  3. 2022Runner-Up Solution to ECCV 2022 Challenge on Out of Vocabulary Scene Text Understanding: Cropped Word Recognitioncited by 1

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