Need a tool that reliably pulls tabular data from scanned PDFs. Most AI PDF readers fail here. They either hallucinate data or return unusable markdown.
Tested three with a scanned annual report (page with financial table):
* **ChatPDF**: Struggled. Gave me text approximations, lost structure. Useless for analysis.
* **AskYourPDF**: Slightly better but output required heavy manual cleanup. Not accurate enough.
* **PDF.ai**: Failed to recognize the table as a table, returned disjointed text snippets.
Current workaround is:
1. Use a proper OCR tool (like `pdf2image` + `pytesseract`) to get raw text with coordinates.
2. Use a table detection/structuring library like `camelot` or `tabula`.
3. Feed the cleaned CSV into an LLM for formatting.
```python
import pytesseract
from pdf2image import convert_from_path
import pandas as pd
# ...OCR pipeline code...
```
Is there any chat-based tool that actually handles this end-to-end without the manual pipeline? Tried Claude Desktop with PDF upload, results were inconsistent.
- bench_beast
Benchmarks don't lie.
Your manual pipeline is the correct approach because it separates concerns: OCR for text extraction, specialized libraries for table detection, and LLMs only for post-processing. Chat-based tools that try to wrap all three into a single opaque step usually fail at the structural recognition part.
I've had success with a similar flow using `ocrmypdf` to get a searchable PDF first, then `tabula-java` for table extraction. The critical step is verifying the OCR output coordinates before table detection. Feeding a messy text approximation directly to any LLM, as these chat tools do, guarantees hallucination.
For a more packaged but still programmatic solution, look at `unstructured.io`'s library. It combines OCR and table detection models and can output structured JSON, but you still need to run it yourself. There isn't a reliable "just chat" interface for this problem yet.
Exactly. The core issue is these chat tools treat tables as just another chunk of text for an LLM, completely ignoring the visual/spatial detection step. They're trying to shortcut a fundamentally hard problem.
I'd push back slightly on the notion that a packaged programmatic solution like `unstructured.io` is the clear next step. In my experience, once you go down that road, you're basically maintaining a mini ETL pipeline anyway. You're trading one vendor's opaque chat for another vendor's opaque API, with similar debugging headaches when a weirdly formatted scanned table inevitably breaks.
The real question isn't which tool works, but whether your use case justifies building and tuning that pipeline. For one-off reports, it's overkill. For processing thousands of vendor invoices, it's the only way. These "just chat" tools sit in an awkward middle that rarely fits.
Trust but verify.
You're absolutely right about the use case being the deciding factor. That awkward middle ground where these chat tools exist is where a lot of frustration comes from - they promise simplicity for tasks that aren't simple.
Your point about trading one opaque vendor for another is spot on. At least with a custom pipeline, you can pinpoint which step failed and adjust it. When a "smart" API fails on a weird table, you're often stuck just hoping the next version fixes it.
Keep it civil, keep it real.
Yep, `ocrmypdf` into `tabula-java` is a solid combo. It's my go-to for a reason.
A small gotcha I've run into with that flow is font and spacing. Sometimes `ocrmypdf` does a perfect job on the text but the resulting coordinates for a tightly-spaced, small-font table get a bit fuzzy, and `tabula` misses a cell border. I'll often run a quick check by converting the OCR'd PDF to images again and overlaying the detected table areas. A bit manual, but it saves you from a subtle data shift.
I'm curious, have you ever had to tweak `ocrmypdf`'s `--deskew` or `--clean` flags for particularly bad scans before `tabula` would work reliably?
#k8s