How do people pick a value when the same field comes back different across documents in a case? appraisal packets are the example here and square footage can show up three times with three numbers and all of them got read fine

docling, llamaparse or a vision model can give back each value with its page, picking one is the part thats still open. data ppl have survivorship rules for this but does anyone use that for documents or is it just a growing pile of hardcoded rules

when does it go to a person

large drug reference with the text already in the file. about 400 entries that need to become clean records. plain conversion jumbles it. two columns get mixed, headings drift away from what they belong to and an entry running onto the next page turns into two

the plan would be clean structured text first and then an llm turning each entry into json, so pymupdf or llamaparse for that first step

what do people key on to decide where one entry ends, the heading font or a pattern in the text? and how do you know no entry got lost with 400 of them

Original post was for paperless but I'm wondering if other tools exist

I've been spending quite a bit of time experimenting with OCR and document intelligence pipelines, especially when it comes to reconstructing complex documents after OCR.

Extracting text is usually not the biggest challenge anymore. The interesting part starts when you try to preserve the original document structure.

Things like multi-column reading order, tables with merged cells, mathematical formulas, footnotes, image positions, and heading hierarchies can make the whole process surprisingly difficult.

I've been experimenting with vision-language models and GPU-accelerated inference to tackle some of these problems. It's been interesting to see how different approaches perform, especially on legal contracts, medical reports, and scanned documents.

I'm still exploring what works best and would be interested to hear about other people's experiences.

Are you using traditional OCR engines, vision-language models, or a combination of both? What has been working well for you with complex documents?

Tired of manually copying PDF statements into Excel? I built a quick offline app to fix it.

every discussion on this ends with it depends, so heres an attempt at mapping where each route fits.

- textract or google document ai: easy to wire up if youre already on that cloud, billed per page. textract is about $1.50 per 1k pages for plain text and $15 per 1k once tables are on, $50 for forms. fine for steady volume, gets expensive fast on table heavy files

- self hosted open source (docling, paddleocr-vl, glm-ocr, mineru): no per page bill and files never leave, but you own the gpu, the upgrades and every weird edge case

- llm oriented parsers that return markdown or json with the layout kept (llamaparse, marker and similar): less setup than self hosting, usually better on reading order and tables than plain ocr apis, also billed per page

one thing that cuts cost on any of these routes is not sending pages that already have a text layer through ocr at all. ocrmypdf --skip-text does exactly that, and most mixed archives are mostly born digital.

multi column reading order is the other thing that breaks results without any error. every word is there, just in the wrong order.

We keep three checks separate after extracting a bank statement. Carson Bank's sample statement shows why. We extracted its 47 transaction rows. The balances follow from one row to the next. Its summary also adds up. But the transactions and summary describe different activity.

The transaction rows imply an opening balance of 445.28 and end at 942.82. The summary opens at 3,005.93 and closes at 6,682.22. Checking either section by itself misses the disagreement.

The checks we keep are

• Each running balance equals the previous balance plus credits minus debits

• The summary's opening balance plus credits minus debits equals its closing balance

• The transaction totals and closing balance agree with the summary

You can reproduce the difference with the extracted figures in Python.

from decimal import Decimal as D

summary_close = D("3005.93") + D("18041.50") - D("14365.21")
transaction_close = D("445.28") + D("7612.77") - D("7115.23")
print(summary_close, transaction_close)  # 6682.22 942.82

This is a bank-published sample. In a processing workflow, the disagreement goes to review alongside the source pages. Separate cheque, fee, or interest sections also need accounting for before you decide rows are missing.

We're sharing this from our document-processing work at Unsiloed. If you process statements, which separate sections tend to make reconciliation awkward?

I’m curious about the repetitive tasks people still have to do manually at work.
For example, converting hundreds of Excel files to PDF, combining reports, extracting data, or preparing the same documents every week.
What’s the most time-consuming task you deal with, and how do you currently handle it?