Ask HN: What's the current best way to extract tables from PDFs?
I am have a set of pdf that are bank statements. The formats of these statements are different based on the bank but they are limited set (<15). What's the current best approach to extract tabular data from PDFs? I tried writing custom logic based on pdfplumber and such but they are very fragile and have lots of ad-hoc logic. The maintenance is pretty high. Are there small models that can run preferably on CPUs alone and that I can possibly fine tune for this task? Any guides or pointers for that? I see a lot of available models, but as someone with no ML background, it's difficult to navigate through.
2 comments
Field report- the problem is subtle. I wrote code to do this for mine, rather than use CSVs, because the statement is a regulated document, which CSVs are not, and it has balances for validation, which CSVs also lack.
I wound up with a pipeline of pdftotext -> configurable regexes to capture the transactions within their respective sections (banks list credits and debits separately without indicating the sign in the amount field) -> BNF parser to turn transaction lines into data, then checks start balance + transactions = end balance.
PITB but works well.
Over the winter will be standing up a local model to see whether a sophisticated prompt can reliably accomplish the same.
Not going to base any workflow on my transaction data on hosted models.
I wound up with a pipeline of pdftotext -> configurable regexes to capture the transactions within their respective sections (banks list credits and debits separately without indicating the sign in the amount field) -> BNF parser to turn transaction lines into data, then checks start balance + transactions = end balance.
PITB but works well.
Over the winter will be standing up a local model to see whether a sophisticated prompt can reliably accomplish the same.
Not going to base any workflow on my transaction data on hosted models.
To extract tables from PDFs, you can use the following tools:
1. Tabula (https://tabula.technology): a free and open-source tool.
2. Parsio (https://parsio.io): uses pre-trained AI models for data extraction from PDFs, emails, and other formats.
3. Airparser (https://airparser.com): uses GPT approach similar to ChatGPT for data extraction from PDFs, emails, and other formats.
1. Tabula (https://tabula.technology): a free and open-source tool.
2. Parsio (https://parsio.io): uses pre-trained AI models for data extraction from PDFs, emails, and other formats.
3. Airparser (https://airparser.com): uses GPT approach similar to ChatGPT for data extraction from PDFs, emails, and other formats.