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The unstructured library provides open-source components for pre-processing text documents such as PDFs, HTML and Word Documents. These components are packaged as bricks 🧱, which provide users the building blocks they need to build pipelines targeted at the documents they care about. Bricks in the library fall into three categories:

• 🧩 Partitioning bricks that break raw documents down into standard, structured elements.
• 🧹 Cleaning bricks that remove unwanted text from documents, such as boilerplate and sentence fragments.
• 🎭 Staging bricks that format data for downstream tasks, such as ML inference and data labeling.

Use the following instructions to get up and running with unstructured and test your installation. NOTE: We do not currently support python 3.11, please use an older version.

• Install the Python SDK with pip install "unstructured[local-inference]" - If you do not need to process PDFs or images, you can run pip install unstructured
• Install the following system dependencies if they are not already available on your system. Depending on what document types you're parsing, you may not need all of these.
  • libmagic-dev (filetype detection)
  • poppler-utils (images and PDFs)
  • tesseract-ocr (images and PDFs)
  • libreoffice (MS Office docs)
• If you are parsing PDFs, run the following to install the detectron2 model, which unstructured uses for layout detection:
  • pip install tensorboard>=2.12.2
  • pip install "detectron2@git+https://github.com/facebookresearch/detectron2.git@e2ce8dc#egg=detectron2"

At this point, you should be able to run the following code:

from unstructured.partition.auto import partition

elements = partition(filename="example-docs/fake-email.eml")
print("\n\n".join([str(el) for el in elements]))

And if you installed with local-inference, you should be able to run this as well:

from unstructured.partition.auto import partition

elements = partition("example-docs/layout-parser-paper.pdf")
print("\n\n".join([str(el) for el in elements]))

The following instructions are intended to help you get up and running using Docker to interact with unstructured. See here if you don't already have docker installed on your machine.

NOTE: we build multi-platform images to support both x86_64 and Apple silicon hardware. docker pull should download the corresponding image for your architecture, but you can specify with --platform (e.g. --platform linux/amd64) if needed.

We build Docker images for all pushes to main. We tag each image with the corresponding short commit hash (e.g. fbc7a69) and the application version (e.g. 0.5.5-dev1). We also tag the most recent image with latest. To leverage this, docker pull from our image repository.

docker pull quay.io/unstructured-io/unstructured:latest

Once pulled, you can create a container from this image and shell to it.

# create the container
docker run -dt --name unstructured quay.io/unstructured-io/unstructured:latest

# this will drop you into a bash shell where the Docker image is running
docker exec -it unstructured bash

You can also build your own Docker image.

If you only plan on parsing one type of data you can speed up building the image by commenting out some of the packages/requirements necessary for other data types. See Dockerfile to know which lines are necessary for your use case.

make docker-build

# this will drop you into a bash shell where the Docker image is running
make docker-start-bash

Once in the running container, you can try things out directly in Python interpreter's interactive mode.

# this will drop you into a python console so you can run the below partition functions
python3

>>> from unstructured.partition.pdf import partition_pdf
>>> elements = partition_pdf(filename="example-docs/layout-parser-paper-fast.pdf")

>>> from unstructured.partition.text import partition_text
>>> elements = partition_text(filename="example-docs/fake-text.txt")

The following instructions are intended to help you get up and running with unstructured locally if you are planning to contribute to the project.

• Using pyenv to manage virtualenv's is recommended but not necessary

  • Mac install instructions. See here for more detailed instructions.
    • brew install pyenv-virtualenv
    • pyenv install 3.8.15
  • Linux instructions are available here.

• Create a virtualenv to work in and activate it, e.g. for one named unstructured:

  pyenv virtualenv 3.8.15 unstructured
pyenv activate unstructured

• Run make install

• Optional:

  • To install models and dependencies for processing images and PDFs locally, run make install-local-inference.
  • For processing image files, tesseract is required. See here for installation instructions.
  • For processing PDF files, tesseract and poppler are required. The pdf2image docs have instructions on installing poppler across various platforms.

Additionally, if you're planning to contribute to unstructured, we provide you an optional pre-commit configuration file to ensure your code matches the formatting and linting standards used in unstructured. If you'd prefer not having code changes auto-tidied before every commit, you can use make check to see whether any linting or formatting changes should be applied, and make tidy to apply them.

If using the optional pre-commit, you'll just need to install the hooks with pre-commit install since the pre-commit package is installed as part of make install mentioned above. Finally, if you decided to use pre-commit you can also uninstall the hooks with pre-commit uninstall.

You can run this Colab notebook to run the examples below.

The following examples show how to get started with the unstructured library.

You can parse TXT, HTML, XML, PDF, EML, MSG, RTF, EPUB, DOC, DOCX, XLSX, CSV, ODT, PPT, PPTX, JPG, and PNG documents with one line of code!

See our documentation page for a full description of the features in the library.

The easiest way to parse a document in unstructured is to use the partition brick. If you use partition brick, unstructured will detect the file type and route it to the appropriate file-specific partitioning brick. If you are using the partition brick, you may need to install additional parameters via pip install unstructured[local-inference]. Ensure you first install libmagic using the instructions outlined here partition will always apply the default arguments. If you need advanced features, use a document-specific brick. The partition brick currently works for .txt, .doc, .docx, .ppt, .pptx, .xlsx, .jpg, .png, .eml, .msg, .html, and .pdf documents.

from unstructured.partition.auto import partition

elements = partition("example-docs/layout-parser-paper.pdf")

Run print("\n\n".join([str(el) for el in elements])) to get a string representation of the output, which looks like:

LayoutParser : A Unified Toolkit for Deep Learning Based Document Image Analysis

Zejiang Shen 1 ( (cid:0) ), Ruochen Zhang 2 , Melissa Dell 3 , Benjamin Charles Germain Lee 4 , Jacob Carlson 3 , and
Weining Li 5

Abstract. Recent advances in document image analysis (DIA) have been primarily driven by the application of neural
networks. Ideally, research outcomes could be easily deployed in production and extended for further investigation.
However, various factors like loosely organized codebases and sophisticated model configurations complicate the easy
reuse of im- portant innovations by a wide audience. Though there have been on-going efforts to improve reusability and
simplify deep learning (DL) model development in disciplines like natural language processing and computer vision, none
of them are optimized for challenges in the domain of DIA. This represents a major gap in the existing toolkit, as DIA
is central to academic research across a wide range of disciplines in the social sciences and humanities. This paper
introduces LayoutParser , an open-source library for streamlining the usage of DL in DIA research and applica- tions.
The core LayoutParser library comes with a set of simple and intuitive interfaces for applying and customizing DL models
for layout de- tection, character recognition, and many other document processing tasks. To promote extensibility,
LayoutParser also incorporates a community platform for sharing both pre-trained models and full document digiti- zation
pipelines. We demonstrate that LayoutParser is helpful for both lightweight and large-scale digitization pipelines in
real-word use cases. The library is publicly available at https://layout-parser.github.io

Keywords: Document Image Analysis · Deep Learning · Layout Analysis · Character Recognition · Open Source library ·
Toolkit.

Introduction

Deep Learning(DL)-based approaches are the state-of-the-art for a wide range of document image analysis (DIA) tasks
including document image classification [11,

You can parse an HTML document using the following workflow:

from unstructured.partition.html import partition_html

elements = partition_html("example-docs/example-10k.html")
print("\n\n".join([str(el) for el in elements[:5]]))

The print statement will show the following text:

UNITED STATES

SECURITIES AND EXCHANGE COMMISSION

Washington, D.C. 20549

FORM 10-K

ANNUAL REPORT PURSUANT TO SECTION 13 OR 15(d) OF THE SECURITIES EXCHANGE ACT OF 1934

And elements will be a list of elements in the HTML document, similar to the following:

[<unstructured.documents.elements.Title at 0x169cbe820>,
 <unstructured.documents.elements.NarrativeText at 0x169cbe8e0>,
 <unstructured.documents.elements.NarrativeText at 0x169cbe3a0>]

You can use the following workflow to parse PDF documents.

from unstructured.partition.pdf import partition_pdf

elements = partition_pdf("example-docs/layout-parser-paper.pdf")

The output will look the same as the example from the document parsing section above.

The partition_email function within unstructured is helpful for parsing .eml files. Common e-mail clients such as Microsoft Outlook and Gmail support exporting e-mails as .eml files. partition_email accepts filenames, file-like object, and raw text as input. The following three snippets for parsing .eml files are equivalent:

from unstructured.partition.email import partition_email

elements = partition_email(filename="example-docs/fake-email.eml")

with open("example-docs/fake-email.eml", "r") as f:
  elements = partition_email(file=f)

with open("example-docs/fake-email.eml", "r") as f:
  text = f.read()
elements = partition_email(text=text)

The elements output will look like the following:

[<unstructured.documents.html.HTMLNarrativeText at 0x13ab14370>,
<unstructured.documents.html.HTMLTitle at 0x106877970>,
<unstructured.documents.html.HTMLListItem at 0x1068776a0>,
<unstructured.documents.html.HTMLListItem at 0x13fe4b0a0>]

Run print("\n\n".join([str(el) for el in elements])) to get a string representation of the output, which looks like:

This is a test email to use for unit tests.

Important points:

Roses are red

Violets are blue

The partition_text function within unstructured can be used to parse simple text files into elements.

partition_text accepts filenames, file-like object, and raw text as input. The following three snippets are for parsing text files:

from unstructured.partition.text import partition_text

elements = partition_text(filename="example-docs/fake-text.txt")

with open("example-docs/fake-text.txt", "r") as f:
  elements = partition_text(file=f)

with open("example-docs/fake-text.txt", "r") as f:
  text = f.read()
elements = partition_text(text=text)

The elements output will look like the following:

[<unstructured.documents.html.HTMLNarrativeText at 0x13ab14370>,
<unstructured.documents.html.HTMLTitle at 0x106877970>,
<unstructured.documents.html.HTMLListItem at 0x1068776a0>,
<unstructured.documents.html.HTMLListItem at 0x13fe4b0a0>]

Run print("\n\n".join([str(el) for el in elements])) to get a string representation of the output, which looks like:

This is a test document to use for unit tests.

Important points:

Hamburgers are delicious

Dogs are the best

I love fuzzy blankets

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