| --- |
| language: |
| - en |
| license: apache-2.0 |
| tags: |
| - image-to-text |
| --- |
| |
| # PARSeq tiny v1.0 |
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| 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. |
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| ## Model description |
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| 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). |
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| ## Intended uses & limitations |
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| You can use the model for STR on images containing Latin characters (62 case-sensitive alphanumeric + 32 punctuation marks). |
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| ### How to use |
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| *TODO* |
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| ### BibTeX entry and citation info |
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| ```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} |
| } |
| ``` |
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|
