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.
翻译:近年来,由于多语种翻译、自主驾驶等应用范围广泛,对文字识别的场景引起了越来越多的兴趣。 在本报告中,我们描述了我们应对词汇外场景文字理解挑战(OOOV-ST)的解决方案,其目的是从自然场景图像中提取词汇外词。 我们基于OOCLIP的模型在小时平均中取得了28.59 ⁇,在ECCV2022 TiE 讲习班的OOOV挑战端至端端的OOOOV字识别轨道中排名第一。