Skip to main content

A library that prepares raw documents for downstream ML tasks.

Project description

Open-Source Pre-Processing Tools for Unstructured Data

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 馃П, 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:

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

Unstructured also provides the capabilities from unstructured as an API. Checkout the unstructured-api repo to get started making API calls. You鈥檒l also find instructions there about how to host your own version of the API.

:eight_pointed_black_star: Quick Start

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+"

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

from import partition

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

The following table shows the document types the unstructured library currently supports. partition will recognize each of these document types and route the document to the appropriate partitioning function. If you already know your document type, you can use the partitioning function listed in the table directly. See our documentation page for more details about the library.

Document Type Partition Function Strategies Table Support Options
CSV Files (.csv) partition_csv N/A Yes None
E-mails (.eml) partition_eml N/A No Encoding
E-mails (.msg) partition_msg N/A No Encoding
EPubs (.epub) partition_epub N/A No Include Page Breaks
Excel Documents (.xlsx/.xls) partition_xlsx N/A Yes None
HTML Pages (.html) partition_html N/A No Encoding; Include Page Breaks
Images (.png/.jpg) partition_image "auto", "hi_res", "ocr_only" Yes Encoding; Include Page Breaks; Infer Table Structure; OCR Languages, Strategy
Markdown (.md) partitin_md N/A No Include Page Breaks
Open Office Documents (.odt) partition_odt N/A Yes None
PDFs (.pdf) partition_pdf "auto", "fast", "hi_res", "ocr_only" Yes Encoding; Include Page Breaks; Infer Table Structure; OCR Languages, Strategy
Plain Text (.txt) partition_text N/A No Encoding, Paragraph Grouper
Power Points (.ppt) partition_ppt N/A Yes Include Page Breaks
Power Points (.pptx) partition_pptx N/A Yes Include Page Breaks
Rich Text Files (.rtf) partition_rtf N/A No Include Page Breaks
Word Documents (.doc) partition_doc N/A Yes None
Word Documents (.docx) partition_docx N/A Yes None
XML Documents (.xml) partition_xml N/A No Encoding; XML Keep Tags

:dizzy: Instructions for using the docker image

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

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

# create the container
docker run -dt --name unstructured

# 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

>>> 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")

:coffee: Installation Instructions for Local Development

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.

:clap: Quick Tour

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 over a dozen document types with one line of code!

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

Document Parsing

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. See the table above for a full list of document types supported in the library.

from 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 Uni铿乪d 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 con铿乬urations complicate the easy
reuse of im- portant innovations by a wide audience. Though there have been on-going e铿orts 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

Keywords: Document Image Analysis 路 Deep Learning 路 Layout Analysis 路 Character Recognition 路 Open Source library 路


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

See the partitioning section in our documentation for a full list of options and instructions on how to use file-specific partitioning functions.

:guardsman: Security Policy

See our security policy for information on how to report security vulnerabilities.

:books: Learn more

Section Description
Company Website product and company info
Documentation Full API documentation
Batch Processing Ingesting batches of documents through Unstructured

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

unstructured-0.7.1.tar.gz (1.3 MB view hashes)

Uploaded source

Built Distribution

unstructured-0.7.1-py3-none-any.whl (1.3 MB view hashes)

Uploaded py3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page