  This report presents our 2nd place solution to ECCV 2022 challenge on
Out-of-Vocabulary Scene Text Understanding (OOV-ST) : Cropped Word Recognition.
This challenge is held in the context of ECCV 2022 workshop on Text in
Everything (TiE), which aims to extract out-of-vocabulary words from natural
scene images. In the competition, we first pre-train SCATTER on the synthetic
datasets, then fine-tune the model on the training set with data augmentations.
Meanwhile, two additional models are trained specifically for long and vertical
texts. Finally, we combine the output from different models with different
layers, different backbones, and different seeds as the final results. Our
solution achieves a word accuracy of 59.45\% when considering out-of-vocabulary
words only.
