[Almost] automatically sort and archive PDF bills and statements.
I get tons of electronic statements each month: from bank statements to credit cards statements to updated insurance policy documents. I store all of them - they don't take much space and you never know when you need to use one of them (I did have a case quite recently, where a receipt from a bank issued 18 years ago had made all the difference, but that's a story for some other day).
One problem with so many receipts is storing them in a way that helps finding things fast (or even finding things at all). Renaming each file by hand sticking to some uniform convention and shuffling those files around gets mind numbingly boring pretty soon.
This is where
classify_bills script comes handy. It goes over all
newly downloaded bills and does a couple of things:
- It tries to figure out what account each document belongs to.
- It then figures out what date this document should be associated with.
- Finally, it names the document according to the set pattern and places it in the right directory.
classify_bills is not
It is not a jack of all trades. It will not download your statements for you (which is a much more complex task given different websites hosting those documents). Neither will it OCR those documents that don't come with text embedded (some places give you a PDF which has no text at all).
It is also not really intelligent. At its core, it is driven by a list of regular expressions.
Still, given the number of bills I get monthly, over the last couple years, it has saved me probably hours of menial, incredibly boring work, so I do consider it a win.
classify_bills is pretty self-contained and can be placed
colorlogger.py is located anywhere where Python can
load it from (current directory, symlink destination,
it will be loaded and output would be colorized.
The script is driven by the configuration file, which is searched in the following order:
Currently, the script has the following external dependencies:
- Python 3.x
pdftotextprogram (usually comes as part of
classify_bills.conf.example to build your own configuration. In
general, a process of adding support for a new kind of bill works the
- Create a new entry in the
- Configure the destination directory and naming convention for bills.
pdftotextto examine text output of a couple bills to find out the following facts:
- Patterns in the text that could be used to uniquely identify this kind of bill (name of a bank or service provider, URLs, etc). It's beter to have several specific patterns to allow future disambiguation between multiple accounts from the same provider (e.g. separate banking and investment bills from the same bank).
- Pattern that could be used to infer the date this bill should be associated with.
- Format of that date.
This process is unfortunately not easy to automate so it has to be done manually and it is a pain. However, it only has to be done once per each account (that is, until the provider decides to change the format of the bill thus breaking the patterns, but it also doesn't happen often).
classify_bills is fairly simple. It can be run either on the
entire directory of input documents (configured via
in the config file), or documents can be explicitly specified in the
command line. By default, the script runs in dry-run mode, not making
any changes. To actually perform all actions, run it with
The script will never overwrite any existing document in the
destination directory (unless it is forced to via
From the overall design point, it is clear that the decision to use JSON as configuration language was not an optimal one. In retrospect, I should've let the configuration be split into individual files, most probably using XML. If I change it in the future, I'll supply a conversion tool. That would also allow individually contributed configurations to be shared.
Furthermore, the most painful aspect of using the tool is manual configuration of patterns for each bill type. There are several ideas on how to try to make it easier: from finding and parsing all dates in the bill to using neural networks to infer those facts from the bill. This might be an interesting direction for future work.