PARSeq small v1.0

PARSeq model pre-trained on various real STR datasets at image size 224x224 with a patch size of 16x16.

Model description

PARSeq (Permuted Autoregressive Sequence) models unify the prevailing modeling/decoding schemes in Scene Text Recognition (STR). In particular, with a single model, it allows for context-free non-autoregressive inference (like CRNN and ViTSTR), context-aware autoregressive inference (like TRBA), and bidirectional iterative refinement (like ABINet).

Intended uses & limitations

You can use the model for STR on images containing Latin characters (62 case-sensitive alphanumeric + 32 punctuation marks).

How to use

TODO

BibTeX entry and citation info

@InProceedings{bautista2022parseq,
  author={Bautista, Darwin and Atienza, Rowel},
  title={Scene Text Recognition with Permuted Autoregressive Sequence Models},
  booktitle={Proceedings of the 17th European Conference on Computer Vision (ECCV)},
  month={10},
  year={2022},
  publisher={Springer International Publishing},
  address={Cham}
}