invoice2data 0.2.40

Python parser to extract data from pdf invoice

# Data extractor for PDF invoices - invoice2data

[](https://circleci.com/gh/m3nu/invoice2data)

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](https://poppler.freedesktop.org/) version. It's included with OSX Homebrew, Debian Sid and Ubuntu 16.04. Without it, `pdftotext` won't parse tables in PDF correctly.

2. 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](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

Currently this is a proof of concept. If you scan your invoices, this could easily be connected to an OCR system. Biggest weakness is the need to manually enter new regexes. I don't see an easy way to make it "learn" new patterns.

Planned features:

- integrate with online OCR
- try to 'guess' parameters for new invoice formats
- can apply machine learning to guess new parameters?

## Contributors
- Alexis de Lattre: Add setup.py for Pypi, fix locale bug, add templates for new invoice types.