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.

本文介绍了 Out-Of-Vocabulary 2022 (OOV) 挑战的最终结果。通过这个挑战，我们发现了一种非常重要的问题，即识别在OCR模型的训练数据集中未见过的现场文本实例。通过构建一个包含326,385个图像的公共数据集，本次挑战收集了4,864,405个场景文本实例，形成了独立的验证和测试集。通过对结果进行深入分析，我们发现当前最先进的模型在新的学习设置下性能略逊。因此，OOV数据集将是未来发展更具弹性和更加通用预测的场景文本模型的必要研究领域。

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