| language: | |
| - en | |
| license: apache-2.0 | |
| tags: | |
| - image-to-text | |
| # PARSeq small v1.0 | |
| PARSeq model pre-trained on various real [STR datasets](https://github.com/baudm/parseq/blob/main/Datasets.md) at image size 128x32 with a patch size of 8x4. | |
| ## 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 | |
| ```bibtex | |
| @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} | |
| } | |
| ``` | |