A command line tool and Python library to support your accounting process.
- extracts text from PDF files using different techniques, like
pdftotext,text,ocrmypdf,pdfminer,pdfplumberor OCR --tesseract, orgvision(Google Cloud Vision). - searches for regex in the result using a YAML or JSON-based template system
- saves results as CSV, JSON or XML or renames PDF files to match the content.
With the flexible template system you can:
- precisely match content PDF files
- plugins available to match line items and tables
- define static fields that are the same for every invoice
- define custom fields needed in your organisation or process
- have multiple regex per field (if layout or wording changes)
- define currency
- extract invoice-items using the
lines-plugin developed by Holger Brunn
Go from PDF files to this:
{'date': (2014, 5, 7), 'invoice_number': '30064443', 'amount': 34.73, 'desc': 'Invoice 30064443 from QualityHosting', 'lines': [{'price': 42.0, 'desc': u'Small Business StandardExchange 2010\nGrundgeb\xfchr pro Einheit\nDienst: OUDJQ_office\n01.05.14-31.05.14\n', 'pos': u'7', 'qty': 1.0}]}
{'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'}
flowchart LR
InvoiceFile[fa:fa-file-invoice Invoicefile\n\npdf\nimage\ntext] --> Input-module(Input Module\n\npdftotext\ntext\npdfminer\npdfplumber\ntesseract\ngvision)
Input-module --> |Extracted Text| C{keyword\nmatching}
Invoice-Templates[(fa:fa-file-lines Invoice Templates)] --> C{keyword\nmatching}
C --> |Extracted Text + fa:fa-file-circle-check Template| E(Template Processing\n apply options from template\nremove accents, replaces etc...)
E --> |Optimized String|Plugins&Parsers(Call plugins + parsers)
subgraph Plugins&Parsers
direction BT
tables[fa:fa-table tables] ~~~ lines[fa:fa-grip-lines lines]
lines ~~~ regex[fa:fa-code regex]
regex ~~~ static[fa:fa-check static]
end
Plugins&Parsers --> |output| result[result\nfa:fa-file-csv,\njson,\nXML]
click Invoice-Templates https://github.com/invoice-x/invoice2data/blob/master/TUTORIAL.md
click result https://github.com/invoice-x/invoice2data#usage
click Input-module https://github.com/invoice-x/invoice2data#installation-of-input-modules
click E https://github.com/invoice-x/invoice2data/blob/master/TUTORIAL.md#options
click tables https://github.com/invoice-x/invoice2data/blob/master/TUTORIAL.md#tables
click lines https://github.com/invoice-x/invoice2data/blob/master/TUTORIAL.md#lines
click regex https://github.com/invoice-x/invoice2data/blob/master/TUTORIAL.md#regex
click static https://github.com/invoice-x/invoice2data/blob/master/TUTORIAL.md#parser-static
- Install pdftotext
If possible get the latest
xpdf/poppler-utils version. It's
included with macOS Homebrew, Debian and Ubuntu. Without it, pdftotext
won't parse tables in PDF correctly.
-
Install
invoice2datausing pippip install invoice2data
An tesseract wrapper is included in auto language mode. It will test your input files against the languages installed on your system. To use it tesseract and imagemagick needs to be installed. tesseract supports multiple OCR engine modes. By default the available engine installed on the system will be used.
Languages: tesseract-ocr recognize more than 100 languages For Linux users, you can often find packages that provide language packs:
# Display a list of all Tesseract language packs
apt-cache search tesseract-ocr
# Debian/Ubuntu users
apt-get install tesseract-ocr-chi-sim # Example: Install Chinese Simplified language pack
# Arch Linux users
pacman -S tesseract-data-eng tesseract-data-deu # Example: Install the English and German language packs
Basic usage. Process PDF files and write result to CSV.
invoice2data invoice.pdfinvoice2data invoice.txtinvoice2data *.pdf
Choose any of the following input readers:
- pdftotext
invoice2data --input-reader pdftotext invoice.pdf - pdftotext
invoice2data --input-reader text invoice.txt - tesseract
invoice2data --input-reader tesseract invoice.pdf - pdfminer.six
invoice2data --input-reader pdfminer invoice.pdf - pdfplumber
invoice2data --input-reader pdfplumber invoice.pdf - ocrmypdf
invoice2data --input-reader ocrmypdf invoice.pdf - gvision
invoice2data --input-reader gvision invoice.pdf(needsGOOGLE_APPLICATION_CREDENTIALSenv var)
Choose any of the following output formats:
- csv
invoice2data --output-format csv invoice.pdf - json
invoice2data --output-format json invoice.pdf - xml
invoice2data --output-format xml invoice.pdf
Save output file with custom name or a specific folder
invoice2data --output-format csv --output-name myinvoices/invoices.csv invoice.pdf
Note: You must specify the output-format in order to create
output-name
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
You can easily add invoice2data to your own Python scripts as library.
from invoice2data import extract_data
result = extract_data('path/to/my/file.pdf')
Using in-house templates
from invoice2data import extract_data
from invoice2data.extract.loader import read_templates
templates = read_templates('/path/to/your/templates/')
result = extract_data(filename, templates=templates)
See invoice2data/extract/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 or JSON. They define one or more keywords to find the right template, one or more exclude_keywords to further narrow it down and regexp for fields to be extracted. They could also be a static value, like the full company name.
Template files are tried in alphabetical order.
We may extend them to feature options to be used during invoice processing.
Example:
issuer: Amazon Web Services, Inc.
keywords:
- Amazon Web Services
exclude_keywords:
- San Jose
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'
lines:
start: Detail
end: \* May include estimated US sales tax
first_line: ^ (?P<description>\w+.*)\$(?P<price_unit>\d+\.\d+)
line: (.*)\$(\d+\.\d+)
skip_line: Note
last_line: VAT \*\*
The lines package has multiple settings:
- start > The pattern where the lines begin. This is typically the header row of the table. This row is not included in the line matching.
- end > The pattern denoting where the lines end. Typically some text at the very end or immediately below the table. Also not included in the line matching.
- first_line > Optional. This is the primary line item for each entry.
- line > If first_line is not provided, this will be used as the primary line pattern. If first_line is provided, this is the pattern for any sub-lines such as line item details.
- skip_line > Optional. If first_line is passed, this pattern indicates which sub-lines will be skipped and their data not recorded. This is useful if tables span multiple pages and you need to skip over page numbers or headers that appear mid-table.
- last_line > Optional. If first_line is passed, this pattern denotes the final line of the sub-lines and is included in the output data.
The performance with yaml templates can be greatly increased 10x by using libyaml
It can be installed on most distributions by:
sudo apt-get libyaml-dev
If you are interested in improving this project, have a look at our developer guide to get you started quickly.
- integrate with online OCR?
- try to 'guess' parameters for new invoice formats.
- can apply machine learning to guess new parameters?
- advanced table parsing with camelot
- Harshit Joshi: As Google Summer of Code student.
- Holger Brunn: Add support for parsing invoice items.
- Odoo, OCA module account_invoice_import_invoice2data
- OCR-Invoice (FOSS | C#)
- DeepLogic AI (Commercial | SaaS)
- Docparser (Commercial | Web Service)
- A-PDF (Commercial)
- PDFdeconstruct (Commercial)
- CVision (Commercial)
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