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--- |
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language: |
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- en |
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license: apache-2.0 |
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tags: |
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- image-to-text |
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--- |
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# 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 |
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@InProceedings{bautista2022parseq, |
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author={Bautista, Darwin and Atienza, Rowel}, |
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title={Scene Text Recognition with Permuted Autoregressive Sequence Models}, |
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booktitle={Proceedings of the 17th European Conference on Computer Vision (ECCV)}, |
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month={10}, |
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year={2022}, |
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publisher={Springer International Publishing}, |
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address={Cham} |
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} |
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``` |
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