knowledge_base/model_doc_layoutlm.txt

LayoutLM

Overview
The LayoutLM model was proposed in the paper LayoutLM: Pre-training of Text and Layout for Document Image
Understanding by Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, and
Ming Zhou. It's a simple but effective pretraining method of text and layout for document image understanding and
information extraction tasks, such as form understanding and receipt understanding. It obtains state-of-the-art results
on several downstream tasks:

form understanding: the FUNSD dataset (a collection of 199 annotated
  forms comprising more than 30,000 words).
receipt understanding: the SROIE dataset (a collection of 626 receipts for
  training and 347 receipts for testing).
document image classification: the RVL-CDIP dataset (a collection of
  400,000 images belonging to one of 16 classes).

The abstract from the paper is the following:
Pre-training techniques have been verified successfully in a variety of NLP tasks in recent years. Despite the
widespread use of pretraining models for NLP applications, they almost exclusively focus on text-level manipulation,
while neglecting layout and style information that is vital for document image understanding. In this paper, we propose
the LayoutLM to jointly model interactions between text and layout information across scanned document images, which is
beneficial for a great number of real-world document image understanding tasks such as information extraction from
scanned documents. Furthermore, we also leverage image features to incorporate words' visual information into LayoutLM.
To the best of our knowledge, this is the first time that text and layout are jointly learned in a single framework for
document-level pretraining. It achieves new state-of-the-art results in several downstream tasks, including form
understanding (from 70.72 to 79.27), receipt understanding (from 94.02 to 95.24) and document image classification
(from 93.07 to 94.42).
Usage tips

In addition to input_ids, [~transformers.LayoutLMModel.forward] also expects the input bbox, which are
  the bounding boxes (i.e. 2D-positions) of the input tokens. These can be obtained using an external OCR engine such
  as Google's Tesseract (there's a Python wrapper available). Each bounding box should be in (x0, y0, x1, y1) format, where
  (x0, y0) corresponds to the position of the upper left corner in the bounding box, and (x1, y1) represents the
  position of the lower right corner. Note that one first needs to normalize the bounding boxes to be on a 0-1000
  scale. To normalize, you can use the following function:

python
def normalize_bbox(bbox, width, height):
    return [
        int(1000 * (bbox[0] / width)),
        int(1000 * (bbox[1] / height)),
        int(1000 * (bbox[2] / width)),
        int(1000 * (bbox[3] / height)),
    ]
Here, width and height correspond to the width and height of the original document in which the token
occurs. Those can be obtained using the Python Image Library (PIL) library for example, as follows:
thon
from PIL import Image
Document can be a png, jpg, etc. PDFs must be converted to images.
image = Image.open(name_of_your_document).convert("RGB")
width, height = image.size

Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with LayoutLM. 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.

A blog post on fine-tuning
  LayoutLM for document-understanding using Keras & Hugging Face
  Transformers.

A blog post on how to fine-tune LayoutLM for document-understanding using only Hugging Face Transformers.

A notebook on how to fine-tune LayoutLM on the FUNSD dataset with image embeddings.

See also: Document question answering task guide

A notebook on how to fine-tune LayoutLM for sequence classification on the RVL-CDIP dataset.
Text classification task guide

A notebook on how to  fine-tune LayoutLM for token classification on the FUNSD dataset.
Token classification task guide

Other resources
- Masked language modeling task guide
🚀 Deploy

A blog post on how to Deploy LayoutLM with Hugging Face Inference Endpoints.

LayoutLMConfig
[[autodoc]] LayoutLMConfig
LayoutLMTokenizer
[[autodoc]] LayoutLMTokenizer
LayoutLMTokenizerFast
[[autodoc]] LayoutLMTokenizerFast

LayoutLMModel
[[autodoc]] LayoutLMModel
LayoutLMForMaskedLM
[[autodoc]] LayoutLMForMaskedLM
LayoutLMForSequenceClassification
[[autodoc]] LayoutLMForSequenceClassification
LayoutLMForTokenClassification
[[autodoc]] LayoutLMForTokenClassification
LayoutLMForQuestionAnswering
[[autodoc]] LayoutLMForQuestionAnswering

TFLayoutLMModel
[[autodoc]] TFLayoutLMModel
TFLayoutLMForMaskedLM
[[autodoc]] TFLayoutLMForMaskedLM
TFLayoutLMForSequenceClassification
[[autodoc]] TFLayoutLMForSequenceClassification
TFLayoutLMForTokenClassification
[[autodoc]] TFLayoutLMForTokenClassification
TFLayoutLMForQuestionAnswering
[[autodoc]] TFLayoutLMForQuestionAnswering