### How to use

Here is how to use this model in PyTorch:

```python
from transformers import TrOCRProcessor, VisionEncoderDecoderModel
from PIL import Image
import requests

# load image from the IAM database (actually this model is meant to be used on printed text)
url = 'https://fki.tic.heia-fr.ch/static/img/a01-122-02-00.jpg'
image = Image.open(requests.get(url, stream=True).raw).convert("RGB")

processor = TrOCRProcessor.from_pretrained('ycchen/TrOCR-base-ver021-v1')
model = VisionEncoderDecoderModel.from_pretrained('ycchen/TrOCR-base-ver021-v1')
pixel_values = processor(images=image, return_tensors="pt").pixel_values

generated_ids = model.generate(pixel_values)
generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
```
{}
transformers pytorch vision-encoder-decoder endpoints_compatible region:us
TAGS
#transformers #pytorch #vision-encoder-decoder #endpoints_compatible #region-us 

### How to use

Here is how to use this model in PyTorch:


### How to use

Here is how to use this model in PyTorch:
TAGS
#transformers #pytorch #vision-encoder-decoder #endpoints_compatible #region-us 
 ### How to use

Here is how to use this model in PyTorch:
passage: TAGS
#transformers #pytorch #vision-encoder-decoder #endpoints_compatible #region-us 
### How to use

Here is how to use this model in PyTorch: