Why Template-Based OCR Breaks at Scale (And What Accurate Extraction Actually Requires)

Your template-based OCR probably works fine right now. It handles your top 10 vendor formats, extracts the fields you need, and keeps AP moving. But there's a volume threshold — somewhere between hundreds and thousands of documents per month — where accuracy starts slipping, processing slows down, and the error rate compounds into a real operational problem. The tool didn't change. Your volume did.

Lido is a document extraction platform that processes any document layout without templates, model training, or page limits. It uses AI vision models and LLMs to read documents the way a person would — understanding structure and context rather than matching against rigid rules. ACS Industries processes 400+ POs weekly through Lido with 100% accuracy after replacing UiPath, which had a 10% failure rate on scanned PDFs.

If you're processing over 1,000 documents per month and noticing more manual corrections, slower throughput, or hard page limits, the issue isn't your team. It's the architecture of your OCR tool. Here's what five companies discovered when they hit that wall.

Lido replaces template-based OCR with AI extraction that reads any document format without per-format configuration. No templates to build, no retraining when layouts change, no accuracy degradation at high volume. Companies processing 10,000+ documents per month use Lido to eliminate the template maintenance treadmill entirely.

Why template-based OCR misreads characters like fives as S

Character-level misreads are the first sign of a template-based OCR system under strain. At low volume, you catch them in review. At high volume, they propagate into your ERP, your billing, and your client records before anyone notices.

A factoring company processing 700,000 pages per year ran into this with their OCR tool. They process 35,000 factoring schedules annually across 400+ clients — every one of which contains dollar amounts, account numbers, and invoice references where a single wrong character means a wrong payment.

Brie Owens, Operations Manager

"Formstorm like reads fives as S, you know what I'm saying? It just doesn't read correctly. So there's a lot of manual that's done."

The misreads weren't occasional. They were systemic. The tool's OCR engine would consistently confuse similar-looking characters — fives rendered as S, ones as lowercase L, zeroes as the letter O. At 700,000 pages per year, even a 2% character error rate means 14,000 pages with bad data flowing downstream. Their team was spending hours each day on manual correction that the OCR tool was supposed to eliminate.

When they tested Lido on the same documents, the extraction came back 100% accurate on sample. No character misreads. No manual corrections needed. The difference wasn't incremental — it was the difference between a system that guesses at characters and one that understands what it's reading.

Why template-based OCR tools hit a page count ceiling

The same factoring company hit a second problem that no amount of error correction could fix: a hard page limit.

Their OCR tool couldn't handle documents over 150 pages. Not "performed poorly" — functionally couldn't. As Brie put it: "Anything over 150 pages in Formstorm, there's such a lag when you're turning the page to verify the data. We don't put anything over 150 pages in Formstorm."

So what happens to the schedules that exceed 150 pages? They get keyed manually. Every line, every field, typed by hand into the system. That's over 3,000 schedules per year that bypass the OCR tool entirely because the tool can't handle the document size.

This isn't a quirk of one product. Page count ceilings and processing speed degradation are structural limitations of template-based OCR. These tools send threads to read data on every single page sequentially, which means processing time scales linearly with page count. A 10-page invoice takes a few seconds. A 150-page schedule takes minutes. A 300-page schedule takes so long the tool becomes unusable.

Lido doesn't have page limits. A telecom expense management firm tested this directly — they processed a 34-page Verizon invoice in 7 seconds. Then they ran 72 carrier invoices through Lido in under 45 minutes. The same batch previously took their full team eight hours.

"I did 72 invoices, Andres, 72, in less than 45 minutes. Where it took the team a full day. Eight hours to process 72." The speed difference reflects a fundamentally different architecture.

Why template-based OCR tools that worked at 100 documents fail at 10,000

The pattern is consistent across industries and tools. A document processing system gets selected based on a demo with clean, simple documents at low volume. It works well enough for the first few months. Then volume grows, document complexity increases, and the system starts failing in ways that weren't visible during evaluation.

A gas distribution company processing 27,000 documents per month — 20,000 invoices, 2,000 supplier statements, 5,000 POs — hit this wall with their extraction tool. The tool couldn't handle nested tables, which appear in a significant portion of their supplier documents. As their team described it, they were "outgrowing some of their capabilities" and looking for "either a full replacement or a supplement to handle the things it can't do."

They'd already invested months in setup and training. They were four calls deep into evaluating Lido as a replacement. The switching cost wasn't the issue — the cost of staying was.

At Soldier Field in Chicago, the AP team was processing 300-400 vendor invoices weekly from different vendors, each with a different format. They tried ChatGPT for extraction. It couldn't handle the variety of vendor-specific layouts. They tried Power Automate. Same problem. Both tools were fine for one-off extractions but fell apart when applied to a diverse, high-volume workflow.

The common thread is that template-based OCR tools are built around a simple assumption: documents are predictable. At low volumes, that assumption holds. You have a manageable number of formats, and you can template each one. But as volume grows, the number of unique formats grows with it. New vendors, new layouts, new edge cases. The tool doesn't scale because the assumption it's built on doesn't scale.

What accurate document extraction at scale actually requires

Accurate extraction at high volume requires four things that template-based OCR tools structurally cannot provide.

1. Speed that doesn't degrade with complexity. If your tool takes longer on complex documents, processing time becomes unpredictable and throughput drops as your document mix gets harder. Lido processed a 34-page carrier invoice in 7 seconds and a batch of 72 invoices in under 2 minutes of extraction time. Processing speed stays consistent regardless of document length or complexity.
2. Accuracy on messy, real-world inputs. Clean digital PDFs are the easy case. The hard case is scanned documents, faxed copies, handwritten fields, and PDFs where characters render ambiguously. A tool that reads fives as S on clean documents will perform worse on scans. Lido uses AI vision models that interpret documents visually — the way a human reader would — rather than relying on character-level OCR that breaks on low-quality inputs.
3. No page limits or document size restrictions. If your tool can't handle documents over 150 pages, you're building a manual workaround into your process for every large document. Lido has no page limits. The factoring company that was manually keying 3,000+ schedules per year because of their tool's page ceiling could process every one of those documents automatically.
4. No retraining when formats change. Template-based and model-trained tools both require human intervention when document layouts change. At scale, layout changes happen constantly — new vendors, updated invoice systems, seasonal formats. Lido doesn't use templates or trained models, so there's nothing to rebuild when a vendor updates their invoice. ACS Industries processes POs from hundreds of vendors through Lido without maintaining a single template.

How Lido processes documents at scale without templates or retraining

Lido uses AI vision models, OCR, and LLMs to extract structured data from any document — invoices, POs, schedules, statements, carrier bills — without templates, model training, or per-format configuration. You upload a document, describe what you need extracted, and get structured data back. When a vendor changes their layout, nothing breaks. When you add a new vendor, there's nothing to set up.

1. No templates to build or maintain
2. No page limits or processing speed degradation on large documents
3. No model training or retraining when formats change
4. Works on scanned PDFs, handwritten fields, and low-quality inputs

ACS Industries processes 400+ POs weekly with 100% accuracy, replacing UiPath which had a 10% failure rate. A telecom expense management firm cut invoice processing from 8 hours to 45 minutes for 72 invoices. Soldier Field saves 20 hours per week on AP processing, down from manual entry to 30 seconds per invoice.

Your OCR tool's accuracy at 100 documents per month is irrelevant if it falls apart at 1,000. The question isn't whether your current tool works today — it's whether it works at the volume you'll be at in six months.