readme: ---
  tags:
  - trocr
  - image-to-text
  widget:
  - src: https://fki.tic.heia-fr.ch/static/img/a01-122-02.jpg
    example_title: Note 1
  - src: https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcSoolxi9yWGAT5SLZShv8vVd0bz47UWRzQC19fDTeE8GmGv_Rn-PCF1pP1rrUx8kOjA4gg&usqp=CAU
    example_title: Note 2
  - src: https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcRNYtTuSBpZPV_nkBYPMFwVVD9asZOPgHww4epu9EqWgDmXW--sE2o8og40ZfDGo87j5w&usqp=CAU
    example_title: Note 3
  ---
  
  # TrOCR (small-sized model, fine-tuned on IAM) 
  
  TrOCR model fine-tuned on the [IAM dataset](https://fki.tic.heia-fr.ch/databases/iam-handwriting-database). 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
  
  # 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-handwritten')
  model = VisionEncoderDecoderModel.from_pretrained('microsoft/trocr-small-handwritten')
  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]
  ```
  
  ### 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}
  }
  ```