Metadata-Version: 1.0
Name: invoice2data
Version: 0.2.24
Summary: Python parser to extract data from pdf invoice
Home-page: https://github.com/m3nu/invoice2data
Author: Manuel Riel
Author-email: github@snapdragon.cc
License: MIT
Description: # Data extractor for PDF invoices - invoice2data
        
        [![Circle CI](https://circleci.com/gh/m3nu/invoice2data.svg?style=svg)](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 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`
        
        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.
        
Platform: UNKNOWN
