  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.
