论文标题
ECCV 2022挑战的第一名解决方案在词汇场景中挑战文本理解：端到端识别词汇单词
1st Place Solution to ECCV 2022 Challenge on Out of Vocabulary Scene Text Understanding: End-to-End Recognition of Out of Vocabulary Words
论文作者
论文摘要
由于其在多语言翻译，自动驾驶等中的广泛应用，我们在近年来引起了近年来越来越多的兴趣。在本报告中，我们描述了我们对词汇表场上的文本理解（OOV-ST）挑战的解决方案，该挑战的目的是从自然场景图像中提取出频率过多（OOV）单词。我们基于OCLIP的模型在H-Mean中达到28.59 \％，在ECCV2022 TIE Workshop中，端到端OOV单词识别曲目排名第一。
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.
