While working on my estimation system, I ran into a problem that looked simple at first:
➡️ “I just need to extract text from PDFs.”
But once I started testing real documents like specifications and requirement files, it became clear that this step was far more critical than I expected.
It wasn’t just about extracting text.
It was about extracting it correctly, cleanly, and especially… fast enough.
Because in my case, extraction is not the final step — it’s the beginning of the whole pipeline.
⚠️ The Real Problem: Latency
At the start, I focused mostly on extraction quality:
- is the text clean?
- are the sections preserved?
- can I detect headings and tables?
But after integrating the extraction into my backend, another issue appeared very quickly:
➡️ latency
Some tools gave excellent results… but were too slow to be usable in a real workflow.
And that changed everything.
Because in a system where users upload documents and expect quick feedback, even a few seconds can break the experience.
🧪 The Tools I Tested
1. pdfplumber
🔗 https://github.com/jsvine/pdfplumber
This was my starting point.
It’s simple, easy to use, and quite fast.
For basic PDFs, it works fine.
But very quickly, I hit its limits:
- the output is mostly plain text
- structure is almost lost
- headings are not clearly identified
- tables are hard to reuse
- reading order can be messy
It’s fast, but too “flat” for what I needed.
2. Docling
🔗 https://github.com/docling-project/docling
Then I tried Docling.
The difference was immediately noticeable.
The extraction was much cleaner, and the document structure was much better preserved.
Headings, sections, and layout made a lot more sense compared to pdfplumber.
For complex documents, it felt like a huge upgrade.
But then came the issue:
➡️ latency again
Docling was significantly slower.
And while the quality was great, the response time made it difficult to use as a default solution in my system.
It felt more like a “high-quality but heavy” option.
3. OpenDataLoader
🔗 https://github.com/opendataloader-project/opendataloader-pdf
Finally, I tested OpenDataLoader.
This is where things started to align better with what I needed.
What I liked immediately:
- it produces structured output (JSON + Markdown)
- it keeps the document logic (headings, lists, tables)
- it’s fast
- and it fits well in a backend workflow
But the most important part:
➡️ it’s much faster
Compared to Docling, the difference in latency was very clear.
And in a system like mine, that mattered more than having perfect extraction.
⚖️ What Really Matters in Practice
At some point, I stopped asking:
“Which tool extracts better?”
And started asking:
“Which tool gives me usable data, fast enough for my system?”
Because in the end, the extraction is just one step in a pipeline that includes:
- chunking
- cleaning
- processing
- and feature extraction
If the first step is too slow, everything else suffers.
🧠 The Trade-Off
Here’s how I see it now:
- pdfplumber → fast, but too basic
- Docling → high quality, but too slow for my use case
- OpenDataLoader → best balance between structure and latency
So I didn’t choose the “best” tool in theory.
I chose the one that fits the real constraints of my system.
🎯 What Changed in My Approach
This comparison helped me rethink how I design my backend.
Instead of focusing only on quality, I now consider:
- latency
- scalability
- integration simplicity
- and how the data will be used later
In my case, a slightly less perfect extraction is acceptable
if it allows the system to stay fast and responsive.
🚀 Final Thought
This experience taught me something simple but important:
In real systems, performance is not optional.
A tool can be technically impressive,
but if it slows down the whole workflow, it becomes hard to use.
Right now, OpenDataLoader gives me the best balance between speed and structure,
and that’s exactly what I need for my estimation system.