|
--- |
|
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} |
|
} |
|
``` |
