Extract structured data from PDF invoices
Python
Latest commit 4f6c02e Feb 3, 2017 Manuel Bump version 0.2.42 > 0.2.43 [skip ci]

README.md

Data extractor for PDF invoices - invoice2data

Circle CI

A Python library to support your accounting process. Tested on Python 2.7, 3.4 and 3.5

  • extracts text from PDF files
  • searches for regex in the result
  • saves results as CSV
  • optionally renames PDF files to match the content

With the flexible template system you can:

  • precisely match PDF files
  • define static fields that are the same for every invoice
  • have multiple regex per field (if layout or wording changes)
  • define currency

Go from PDF files to this:

{'date': (2014, 5, 7), 'invoice_number': '30064443', 'amount': 34.73, 'desc': 'Invoice 30064443 from QualityHosting'}
{'date': (2014, 6, 4), 'invoice_number': 'EUVINS1-OF5-DE-120725895', 'amount': 35.24, 'desc': 'Invoice EUVINS1-OF5-DE-120725895 from Amazon EU'}
{'date': (2014, 8, 3), 'invoice_number': '42183017', 'amount': 4.11, 'desc': 'Invoice 42183017 from Amazon Web Services'}
{'date': (2015, 1, 28), 'invoice_number': '12429647', 'amount': 101.0, 'desc': 'Invoice 12429647 from Envato'}

Installation

  1. Install pdftotext

If possible get the latest xpdf/poppler-utils version. It's included with OSX Homebrew, Debian Sid and Ubuntu 16.04. Without it, pdftotext won't parse tables in PDF correctly.

  1. Install invoice2data using pip
pip install invoice2data

Optionally this uses pdfminer, but pdftotext works better. You can choose which module to use. No special Python packages are necessary at the moment, except for pdftotext.

There is also tesseract integration as a fallback, if no text can be extracted. But it may be more reliable to use

Usage

Basic usage. Process PDF files and write result to CSV.

  • invoice2data invoice.pdf
  • invoice2data *.pdf

Specify folder with yml templates. (e.g. your suppliers) invoice2data --template-folder ACME-templates invoice.pdf

Only use your own templates and exclude built-ins invoice2data --exclude-built-in-templates --template-folder ACME-templates invoice.pdf

Processes a folder of invoices and copies renamed invoices to new folder. invoice2data --copy new_folder folder_with_invoices/*.pdf

Processes a single file and dumps whole file for debugging (useful when adding new templates in templates.py) invoice2data --debug my_invoice.pdf

Recognize test invoices: invoice2data invoice2data/test/pdfs/* --debug

If you want to use it as a lib just do

from invoice2data import extract_data

result = extract_data('path/to/my/file.pdf')

Template system

See invoice2data/templates for existing templates. Just extend the list to add your own. If deployed by a bigger organisation, there should be an interface to edit templates for new suppliers. 80-20 rule. For a short tutorial on how to add new templates, see TUTORIAL.md.

Templates are based on Yaml. They define one or more keywords to find the right template and regexp for fields to be extracted. They could also be a static value, like the full company name.

We may extend them to feature options to be used during invoice processing.

Example:

issuer: Amazon Web Services, Inc.
keywords:
- Amazon Web Services
fields:
  amount: TOTAL AMOUNT DUE ON.*\$(\d+\.\d+)
  amount_untaxed: TOTAL AMOUNT DUE ON.*\$(\d+\.\d+)
  date: Invoice Date:\s+([a-zA-Z]+ \d+ , \d+)
  invoice_number: Invoice Number:\s+(\d+)
  partner_name: (Amazon Web Services, Inc\.)
options:
  remove_whitespace: false
  currency: HKD
  date_formats:
    - '%d/%m/%Y'

Roadmap and open tasks

  • tutorial and documentation for template options.
  • parse invoice items.
  • integrate with online OCR?
  • try to 'guess' parameters for new invoice formats.
  • can apply machine learning to guess new parameters?

Maintainers

Other Contributors