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* Map model_type and doc pages names * Add script * Fix typo * Quality * Manual check for Auto Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr>
102 lines
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102 lines
4.8 KiB
Plaintext
<!--Copyright 2021 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an
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"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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# TrOCR
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## Overview
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The TrOCR model was proposed in [TrOCR: Transformer-based Optical Character Recognition with Pre-trained
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Models](https://arxiv.org/abs/2109.10282) by Minghao Li, Tengchao Lv, Lei Cui, Yijuan Lu, Dinei Florencio, Cha Zhang,
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Zhoujun Li, Furu Wei. TrOCR consists of an image Transformer encoder and an autoregressive text Transformer decoder to
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perform [optical character recognition (OCR)](https://en.wikipedia.org/wiki/Optical_character_recognition).
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The abstract from the paper is the following:
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*Text recognition is a long-standing research problem for document digitalization. Existing approaches for text recognition
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are usually built based on CNN for image understanding and RNN for char-level text generation. In addition, another language
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model is usually needed to improve the overall accuracy as a post-processing step. In this paper, we propose an end-to-end
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text recognition approach with pre-trained image Transformer and text Transformer models, namely TrOCR, which leverages the
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Transformer architecture for both image understanding and wordpiece-level text generation. The TrOCR model is simple but
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effective, and can be pre-trained with large-scale synthetic data and fine-tuned with human-labeled datasets. Experiments
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show that the TrOCR model outperforms the current state-of-the-art models on both printed and handwritten text recognition
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tasks.*
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<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/trocr_architecture.jpg"
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alt="drawing" width="600"/>
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<small> TrOCR architecture. Taken from the <a href="https://arxiv.org/abs/2109.10282">original paper</a>. </small>
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Please refer to the [`VisionEncoderDecoder`] class on how to use this model.
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This model was contributed by [nielsr](https://huggingface.co/nielsr). The original code can be found
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[here](https://github.com/microsoft/unilm/tree/6f60612e7cc86a2a1ae85c47231507a587ab4e01/trocr).
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Tips:
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- The quickest way to get started with TrOCR is by checking the [tutorial
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notebooks](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/TrOCR), which show how to use the model
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at inference time as well as fine-tuning on custom data.
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- TrOCR is pre-trained in 2 stages before being fine-tuned on downstream datasets. It achieves state-of-the-art results
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on both printed (e.g. the [SROIE dataset](https://paperswithcode.com/dataset/sroie) and handwritten (e.g. the [IAM
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Handwriting dataset](https://fki.tic.heia-fr.ch/databases/iam-handwriting-database>) text recognition tasks. For more
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information, see the [official models](https://huggingface.co/models?other=trocr>).
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- TrOCR is always used within the [VisionEncoderDecoder](vision-encoder-decoder) framework.
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## Inference
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TrOCR's [`VisionEncoderDecoder`] model accepts images as input and makes use of
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[`~generation_utils.GenerationMixin.generate`] to autoregressively generate text given the input image.
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The [`ViTFeatureExtractor`] class is responsible for preprocessing the input image and
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[`RobertaTokenizer`] decodes the generated target tokens to the target string. The
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[`TrOCRProcessor`] wraps [`ViTFeatureExtractor`] and [`RobertaTokenizer`]
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into a single instance to both extract the input features and decode the predicted token ids.
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- Step-by-step Optical Character Recognition (OCR)
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``` py
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>>> from transformers import TrOCRProcessor, VisionEncoderDecoderModel
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>>> import requests
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>>> from PIL import Image
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>>> processor = TrOCRProcessor.from_pretrained("microsoft/trocr-base-handwritten")
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>>> model = VisionEncoderDecoderModel.from_pretrained("microsoft/trocr-base-handwritten")
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>>> # load image from the IAM dataset url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02.jpg"
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>>> image = Image.open(requests.get(url, stream=True).raw).convert("RGB")
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>>> pixel_values = processor(image, return_tensors="pt").pixel_values
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>>> generated_ids = model.generate(pixel_values)
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>>> generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
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```
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See the [model hub](https://huggingface.co/models?filter=trocr) to look for TrOCR checkpoints.
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## TrOCRConfig
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[[autodoc]] TrOCRConfig
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## TrOCRProcessor
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[[autodoc]] TrOCRProcessor
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- __call__
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- from_pretrained
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- save_pretrained
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- batch_decode
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- decode
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- as_target_processor
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## TrOCRForCausalLM
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[[autodoc]] TrOCRForCausalLM
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- forward
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