TY  - CONF
AU  - Sergi Garcia Bordils
AU  - Andres Mafla
AU  - Ali Furkan Biten
AU  - Oren Nuriel
AU  - Aviad Aberdam
AU  - Shai Mazor
AU  - Ron Litman
AU  - Dimosthenis Karatzas
A2  - ECCVW
PY  - 2022//
TI  - Out-of-Vocabulary Challenge Report
T2  - LNCS
BT  - Proceedings European Conference on Computer Vision Workshops
SP  - 359–375
VL  - 13804
N2  - 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.
UR  - https://link.springer.com/chapter/10.1007/978-3-031-25069-9_24
L1  - http://refbase.cvc.uab.es/files/GMB2022.pdf
UR  - http://dx.doi.org/10.1007/978-3-031-25069-9_24
N1  - DAG; 600.155; 302.105; 611.002
ID  - Sergi Garcia Bordils2022
ER  -