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# TrOCR

## Overview

The TrOCR model was proposed in [TrOCR: Transformer-based Optical Character Recognition with Pre-trained
Models](https://arxiv.org/abs/2109.10282) by Minghao Li, Tengchao Lv, Lei Cui, Yijuan Lu, Dinei Florencio, Cha Zhang,
Zhoujun Li, Furu Wei. TrOCR consists of an image Transformer encoder and an autoregressive text Transformer decoder to
perform [optical character recognition (OCR)](https://en.wikipedia.org/wiki/Optical_character_recognition).

The abstract from the paper is the following:

*Text recognition is a long-standing research problem for document digitalization. Existing approaches for text recognition
are usually built based on CNN for image understanding and RNN for char-level text generation. In addition, another language
model is usually needed to improve the overall accuracy as a post-processing step. In this paper, we propose an end-to-end
text recognition approach with pre-trained image Transformer and text Transformer models, namely TrOCR, which leverages the
Transformer architecture for both image understanding and wordpiece-level text generation. The TrOCR model is simple but
effective, and can be pre-trained with large-scale synthetic data and fine-tuned with human-labeled datasets. Experiments
show that the TrOCR model outperforms the current state-of-the-art models on both printed and handwritten text recognition
tasks.*

<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/trocr_architecture.jpg"
alt="drawing" width="600"/> 

<small> TrOCR architecture. Taken from the [original paper](https://arxiv.org/abs/2109.10282). </small>

Please refer to the [`VisionEncoderDecoder`] class on how to use this model.

This model was contributed by [nielsr](https://huggingface.co/nielsr). The original code can be found
[here](https://github.com/microsoft/unilm/tree/6f60612e7cc86a2a1ae85c47231507a587ab4e01/trocr).

Tips:

- The quickest way to get started with TrOCR is by checking the [tutorial
  notebooks](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/TrOCR), 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](https://paperswithcode.com/dataset/sroie) and handwritten (e.g. the [IAM
  Handwriting dataset](https://fki.tic.heia-fr.ch/databases/iam-handwriting-database>) text recognition tasks. For more
  information, see the [official models](https://huggingface.co/models?other=trocr>).
- TrOCR is always used within the [VisionEncoderDecoder](./model_doc/visionencoderdecoder) framework.

## Inference

TrOCR's [`VisionEncoderDecoder`] model accepts images as input and makes use of
[`~generation_utils.GenerationMixin.generate`] to autoregressively generate text given the input image.

The [`ViTFeatureExtractor`] class is responsible for preprocessing the input image and
[`RobertaTokenizer`] decodes the generated target tokens to the target string. The
[`TrOCRProcessor`] wraps [`ViTFeatureExtractor`] and [`RobertaTokenizer`]
into a single instance to both extract the input features and decode the predicted token ids.

- Step-by-step Optical Character Recognition (OCR)

``` py
>>> from transformers import TrOCRProcessor, VisionEncoderDecoderModel
>>> import requests 
>>> from PIL import Image

>>> processor = TrOCRProcessor.from_pretrained("microsoft/trocr-base-handwritten") 
>>> model = VisionEncoderDecoderModel.from_pretrained("microsoft/trocr-base-handwritten")

>>> # load image from the IAM dataset url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02.jpg" 
>>> image = Image.open(requests.get(url, stream=True).raw).convert("RGB")

>>> pixel_values = processor(image, return_tensors="pt").pixel_values 
>>> generated_ids = model.generate(pixel_values)

>>> generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0] 
```

See the [model hub](https://huggingface.co/models?filter=trocr) to look for TrOCR checkpoints.

## TrOCRConfig

[[autodoc]] TrOCRConfig

## TrOCRProcessor

[[autodoc]] TrOCRProcessor
    - __call__
    - from_pretrained
    - save_pretrained
    - batch_decode
    - decode
    - as_target_processor

## TrOCRForCausalLM

[[autodoc]] TrOCRForCausalLM
     - forward
