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--- |
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tags: |
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- mgp-str |
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- image-to-text |
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widget: |
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- src: https://github.com/AlibabaResearch/AdvancedLiterateMachinery/blob/main/OCR/MGP-STR/demo_imgs/IIIT5k_HOUSE.png |
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example_title: Example 1 |
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- src: https://github.com/AlibabaResearch/AdvancedLiterateMachinery/blob/main/OCR/MGP-STR/demo_imgs/IIT5k_EVERYONE.png |
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example_title: Example 2 |
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- src: https://github.com/AlibabaResearch/AdvancedLiterateMachinery/blob/main/OCR/MGP-STR/demo_imgs/CUTE80_KINGDOM.png |
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example_title: Example 3 |
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--- |
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# MGP-STR (base-sized model) |
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MGP-STR base-sized model is trained on MJSynth and SynthText. It was introduced in the paper [Multi-Granularity Prediction for Scene Text Recognition](https://arxiv.org/abs/2209.03592) and first released in [this repository](https://github.com/AlibabaResearch/AdvancedLiterateMachinery/tree/main/OCR/MGP-STR). |
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## Model description |
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MGP-STR is pure vision STR model, consisting of ViT and specially designed A^3 modules. The ViT module was initialized from the weights of DeiT-base, except the patch embedding model, due to the inconsistent input size. |
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Images (32x128) are presented to the model as a sequence of fixed-size patches (resolution 4x4), which are linearly embedded. One also adds absolute position embeddings before feeding the sequence to the layers of the ViT module. Next, A^3 module selects a meaningful combination from the tokens of ViT output and integrates them into one output token corresponding to a specific character. Moreover, subword classification heads based on BPE A^3 module and WordPiece A^3 module are devised for subword predictions, so that the language information can be implicitly modeled. Finally, these multi-granularity predictions (character, subword and even word) are merged via a simple and effective fusion strategy. |
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## Intended uses & limitations |
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You can use the raw model for optical character recognition (OCR) on text images. See the [model hub](https://huggingface.co/models?search=alibaba-damo/mgp-str) to look for fine-tuned versions on a task that interests you. |
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### How to use |
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Here is how to use this model in PyTorch: |
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```python |
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from transformers import MgpstrProcessor, MgpstrForSceneTextRecognition |
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import requests |
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from PIL import Image |
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processor = MgpstrProcessor.from_pretrained('alibaba-damo/mgp-str-base') |
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model = MgpstrForSceneTextRecognition.from_pretrained('alibaba-damo/mgp-str-base') |
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# load image from the IIIT-5k dataset |
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url = "https://i.postimg.cc/ZKwLg2Gw/367-14.png" |
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image = Image.open(requests.get(url, stream=True).raw).convert("RGB") |
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pixel_values = processor(images=image, return_tensors="pt").pixel_values |
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outputs = model(pixel_values) |
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generated_text = processor.batch_decode(outputs.logits)['generated_text'] |
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``` |
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### BibTeX entry and citation info |
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```bibtex |
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@inproceedings{ECCV2022mgp_str, |
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title={Multi-Granularity Prediction for Scene Text Recognition}, |
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author={Peng Wang, Cheng Da, and Cong Yao}, |
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booktitle = {ECCV}, |
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year={2022} |
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} |
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``` |
