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The problem was that digitized receipts were not machine-readable and we could not afford to properly run OCR in all images we had (although we've tried). However, a couple of months ago the Chamber of Deputies started to offer eletronic receipts.
Since we know their URL (thanks @giovanisleite for #501) and they are structured HTML documens (that is to say, machine-readable), we can now try a classifier that idenfies alcoholic beverages in the reimbursements (what is not allowed).
We just need to take extra care to check whether the full amount of the eletronic receipt was actually reimbursed (even without remark, sometimes the Chamber of Deputies cuts off alcholic beverages from the reimbusements).
What is the problem?
The problem was that digitized receipts were not machine-readable and we could not afford to properly run OCR in all images we had (although we've tried). However, a couple of months ago the Chamber of Deputies started to offer eletronic receipts.
Since we know their URL (thanks @giovanisleite for #501) and they are structured HTML documens (that is to say, machine-readable), we can now try a classifier that idenfies alcoholic beverages in the reimbursements (what is not allowed).
We just need to take extra care to check whether the full amount of the eletronic receipt was actually reimbursed (even without remark, sometimes the Chamber of Deputies cuts off alcholic beverages from the reimbusements).
How can this be addressed?
I think the classifier should:
Surely we might go first to a exploratory notebook at github.com/okfn-brasil/notebooks to test whether results are worth it!
Who could help with this issue?
Anyone 💜
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