show that the TrOCR model outperforms the current state-of-the-art models on both printed and handwritten text recognition
tasks.
 
 TrOCR architecture. Taken from the original paper. 
Please refer to the [VisionEncoderDecoder] class on how to use this model.
This model was contributed by nielsr. The original code can be found
here.
Tips:
The quickest way to get started with TrOCR is by checking the tutorial
  notebooks, which show how to use the model
  at inference time as well as fine-tuning on custom data.
TrOCR is pre-trained in 2 stages before being fine-tuned on downstream datasets. It achieves state-of-the-art results
  on both printed (e.g. the SROIE dataset and handwritten (e.g. the IAM
  Handwriting dataset text recognition tasks. For more
  information, see the official models.
