readme: ---
  tags:
  - mgp-str
  - image-to-text
  widget:
  - src: https://github.com/AlibabaResearch/AdvancedLiterateMachinery/blob/main/OCR/MGP-STR/demo_imgs/IIIT5k_HOUSE.png
    example_title: Example 1
  - src: https://github.com/AlibabaResearch/AdvancedLiterateMachinery/blob/main/OCR/MGP-STR/demo_imgs/IIT5k_EVERYONE.png
    example_title: Example 2
  - src: https://github.com/AlibabaResearch/AdvancedLiterateMachinery/blob/main/OCR/MGP-STR/demo_imgs/CUTE80_KINGDOM.png
    example_title: Example 3
  ---
  
  # MGP-STR (base-sized model) 
  
  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). 
  
  ## Model description
  
  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.
  
  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.
  
  ## Intended uses & limitations
  
  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.
  
  ### How to use
  
  Here is how to use this model in PyTorch:
  
  ```python
  from transformers import MGPSTRProcessor, MGPSTRModel
  import requests
  from PIL import Image
  
  processor = MGPSTRProcessor.from_pretrained('alibaba-damo/mgp-str-base')
  model = MGPSTRModel.from_pretrained('alibaba-damo/mgp-str-base')
  
  # load image from the IIIT-5k dataset
  url = "https://i.postimg.cc/ZKwLg2Gw/367-14.png"
  image = Image.open(requests.get(url, stream=True).raw).convert("RGB")
  
  pixel_values = processor(image, return_tensors="pt").pixel_values
  generated_ids, attens = model(pixel_values)
  
  generated_text = processor.batch_decode(generated_ids)['generated_text']
  ```
  
  ### BibTeX entry and citation info
  
  ```bibtex
  @inproceedings{ECCV2022mgp_str,
    title={Multi-Granularity Prediction for Scene Text Recognition},
    author={Peng Wang, Cheng Da, and Cong Yao},
    booktitle = {ECCV},
    year={2022}
  }
  ```