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docs/source/en/model_doc/trocr.mdx

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+<!--Copyright 2021 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the
+License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an
+"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License. -->
+
+# 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 <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).
+
+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.
+
+## 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`/`DeiTFeatureExtractor`] class is responsible for preprocessing the input image and
+[`RobertaTokenizer`/`XLMRobertaTokenizer`] decodes the generated target tokens to the target string. The
+[`TrOCRProcessor`] wraps [`ViTFeatureExtractor`/`DeiTFeatureExtractor`] 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
+    - as_target_processor
+
+## TrOCRForCausalLM
+
+[[autodoc]] TrOCRForCausalLM
+     - forward