TrOCR
Source: transformers

# TrOCR## OverviewThe TrOCR model was proposed in [TrOCR: Transformer-based Optical Character Recognition with Pre-trainedModels](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 toperform [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 recognitionare usually built based on CNN for image understanding and RNN for char-level text generation. In addition, another languagemodel is usually needed to improve the overall accuracy as a post-processing step. In this paper, we propose an end-to-endtext recognition approach with pre-trained image Transformer and text Transformer models, namely TrOCR, which leverages theTransformer architecture for both image understanding and wordpiece-level text generation. The TrOCR model is simple buteffective, and can be pre-trained with large-scale synthetic data and fine-tuned with human-labeled datasets. Experimentsshow that the TrOCR model outperforms the current state-of-the-art models on both printed and handwritten text recognitiontasks.*<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/trocr_architecture.jpg"alt="drawing" width="600"/><small> TrOCR architecture. Taken from the <a href="https://arxiv.org/abs/2109.10282">original paper</a>. </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).## Usage 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](vision-encoder-decoder) framework.## ResourcesA list of official Hugging Face and community (indicated by 🌎) resources to help you get started with TrOCR. If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.<PipelineTag pipeline="text-classification"/>- A blog post on [Accelerating Document AI](https://huggingface.co/blog/document-ai) with TrOCR.- A blog post on how to [Document AI](https://github.com/philschmid/document-ai-transformers) with TrOCR.- A notebook on how to [finetune TrOCR on IAM Handwriting Database using Seq2SeqTrainer](https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/TrOCR/Fine_tune_TrOCR_on_IAM_Handwriting_Database_using_Seq2SeqTrainer.ipynb).- A notebook on [inference with TrOCR](https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/TrOCR/Inference_with_TrOCR_%2B_Gradio_demo.ipynb) and Gradio demo.- A notebook on [finetune TrOCR on the IAM Handwriting Database](https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/TrOCR/Fine_tune_TrOCR_on_IAM_Handwriting_Database_using_native_PyTorch.ipynb) using native PyTorch.- A notebook on [evaluating TrOCR on the IAM test set](https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/TrOCR/Evaluating_TrOCR_base_handwritten_on_the_IAM_test_set.ipynb).<PipelineTag pipeline="text-generation"/>- [Casual language modeling](https://huggingface.co/docs/transformers/tasks/language_modeling) task guide.⚡️ Inference- An interactive-demo on [TrOCR handwritten character recognition](https://huggingface.co/spaces/nielsr/TrOCR-handwritten).## InferenceTrOCR's [`VisionEncoderDecoder`] model accepts images as input and makes use of[`~generation.GenerationMixin.generate`] to autoregressively generate text given the input image.The [`ViTImageProcessor`/`DeiTImageProcessor`] class is responsible for preprocessing the input image and[`RobertaTokenizer`/`XLMRobertaTokenizer`] decodes the generated target tokens to the target string. The[`TrOCRProcessor`] wraps [`ViTImageProcessor`/`DeiTImageProcessor`] and [`RobertaTokenizer`/`XLMRobertaTokenizer`]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## TrOCRForCausalLM[[autodoc]] TrOCRForCausalLM     - forward