论文标题
Vocabulary挑战报告
Out-of-Vocabulary Challenge Report
论文作者
论文摘要
本文提出了2022年销量超出量的挑战的最终结果。 OOV竞赛介绍了一个重要方面，而光学特征识别（OCR）模型通常不会研究，即在训练时对看不见的场景文本实例的识别。竞赛编制了包含326,385张图像的公共场景文本数据集的集合，其中包括4,864,405场景文本实例，从而涵盖了广泛的数据分布。形成了一个新的独立验证和测试集，其中包括在训练时词汇量不超出词汇的场景文本实例。竞争是在两项任务中进行的，分别是端到端和裁剪的文本识别。介绍了基准和不同参与者的结果的详尽分析。有趣的是，在新研究的设置下，当前的最新模型显示出显着的性能差距。我们得出的结论是，在此挑战中提出的OOV数据集将是要探索的重要领域，以开发现场文本模型，以实现更健壮和广义的预测。
This paper presents final results of the Out-Of-Vocabulary 2022 (OOV) challenge. The OOV contest introduces an important aspect that is not commonly studied by Optical Character Recognition (OCR) models, namely, the recognition of unseen scene text instances at training time. The competition compiles a collection of public scene text datasets comprising of 326,385 images with 4,864,405 scene text instances, thus covering a wide range of data distributions. A new and independent validation and test set is formed with scene text instances that are out of vocabulary at training time. The competition was structured in two tasks, end-to-end and cropped scene text recognition respectively. A thorough analysis of results from baselines and different participants is presented. Interestingly, current state-of-the-art models show a significant performance gap under the newly studied setting. We conclude that the OOV dataset proposed in this challenge will be an essential area to be explored in order to develop scene text models that achieve more robust and generalized predictions.
