I wrote this package to support my accounting process.
- extracts text from PDF files
- searches for regex in the result
- saves results as CSV
- optionally renames PDF files to match the content
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'}
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
Processes a folder of invoices and copies renamed invoices to new folder.
python -m invoice2data.main folder_with_invoices --copy new_folder
Processes a single file and dumps whole file for debugging (useful when adding new templates in templates.py)
python -m invoice2data.main --debug my_invoice.pdf
Recognize test invoices:
python -m invoice2data.main invoice2data/test/pdfs --debug
See invoice2data/templates.py 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.
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?