Upload README.md

README.md

@@ -1,3 +1,59 @@
 ---
-license: apache-2.0
+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}
+}
+```