readme: ---
  tags:
  - trocr
  - image-to-text
  ---
  
  # TrOCR (small-sized model, pre-trained only) 
  
  TrOCR pre-trained only model. It was introduced in the paper [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://arxiv.org/abs/2109.10282) by Li et al. and first released in [this repository](https://github.com/microsoft/unilm/tree/master/trocr). 
  
  
  ## Model description
  
  The TrOCR model is an encoder-decoder model, consisting of an image Transformer as encoder, and a text Transformer as decoder. The image encoder was initialized from the weights of DeiT, while the text decoder was initialized from the weights of UniLM.
  
  Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder. Next, the Transformer text decoder autoregressively generates tokens.
  
  ## Intended uses & limitations
  
  You can use the raw model for optical character recognition (OCR) on single text-line images. See the [model hub](https://huggingface.co/models?search=microsoft/trocr) 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 TrOCRProcessor, VisionEncoderDecoderModel
  from PIL import Image
  import requests
  import torch
  
  # load image from the IAM database
  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('microsoft/trocr-small-stage1')
  model = VisionEncoderDecoderModel.from_pretrained('microsoft/trocr-small-stage1')
  
  # training
  pixel_values = processor(image, return_tensors="pt").pixel_values  # Batch size 1
  decoder_input_ids = torch.tensor([[model.config.decoder.decoder_start_token_id]])
  outputs = model(pixel_values=pixel_values, decoder_input_ids=decoder_input_ids)
  ```
  
  ### BibTeX entry and citation info
  
  ```bibtex
  @misc{li2021trocr,
        title={TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models}, 
        author={Minghao Li and Tengchao Lv and Lei Cui and Yijuan Lu and Dinei Florencio and Cha Zhang and Zhoujun Li and Furu Wei},
        year={2021},
        eprint={2109.10282},
        archivePrefix={arXiv},
        primaryClass={cs.CL}
  }
  ```