Evaluating check processing software? See how AI-powered extraction compares to template-based OCR on accuracy, setup time, and total cost of ownership.
Drop in a personal check, business check, or cashier’s check—even a difficult one with handwriting, watermarks, or poor image quality. The free trial gives you 50 pages to evaluate.
Watch the engine identify payee, amount, date, check number, memo, and MICR data instantly. Each field includes a confidence score so you can judge accuracy on your specific check types.
Test personal checks, business checks, and cashier’s checks side by side. Export results as Excel, CSV, or JSON and benchmark against your current OCR solution before you commit.
“We tested four check OCR vendors before choosing Lido. It was the only one that handled checks from all 30 of our client banks without any template setup.”
“Our previous OCR tool needed a new template every time a vendor switched banks. With Lido we just upload and it works. We eliminated two hours of weekly template maintenance.”
“The side-by-side comparison was clear. Lido matched our legacy tool’s accuracy on printed checks and vastly outperformed it on handwritten ones.”
Audited controls over a sustained period, not a point-in-time check.
Bank-grade encryption at rest and TLS 1.2+ in transit.
Documents deleted within 24 hours. No copies retained.
The best check OCR software eliminates manual data entry by reading check images—scans, photos, or PDFs—and extracting payee name, date, amount, routing number, account number, and check number into structured rows. The right tool for a given team depends on check volume, the variety of check formats received, whether handwritten fields are common, and what downstream system the data needs to reach.
Template-based check OCR tools require you to draw zones on a sample check image and map each zone to a field. This works well if every check you process comes from the same bank with the same layout. However, most businesses receive checks from dozens or hundreds of sources. Each new bank layout means a new template, and any time a bank updates its check design, the template breaks. Over time, template maintenance becomes a significant operational cost that erodes the time savings OCR was supposed to deliver.
AI-powered check OCR takes a fundamentally different approach. Instead of relying on fixed coordinates, the AI reads the check the way a human would—recognizing that the number in the box is the amount, the name on the “Pay to the order of” line is the payee, and the string of digits at the bottom is the MICR line. Lido uses this layout-agnostic approach to process checks from any bank on the first upload, with no template creation and no training data required.
When comparing check OCR vendors, run a proof-of-concept with your actual check images rather than relying on demo data. Test handwritten checks specifically, since that is where the gap between AI and traditional OCR is widest. Ask about per-field confidence scoring, which lets you set a review threshold instead of spot-checking every extraction. And verify that the output format matches your accounting workflow—whether that means CSV import into QuickBooks, direct API integration with your ERP, or Excel files for manual reconciliation.
The most important criteria when evaluating check OCR software are accuracy on handwritten fields, support for multiple check formats without per-format configuration, output flexibility for downstream accounting systems, and compliance certifications like SOC 2 Type 2. Template-free AI extraction is now the standard for teams processing checks from more than a handful of sources, because it eliminates the setup and maintenance cost of per-layout templates.
Traditional template-based OCR requires you to define fixed zones on the check image where each field appears. This works when every check follows the same layout, but breaks when you receive checks from different banks or in different formats. AI check OCR uses vision models that read the check contextually, identifying the payee, amount, date, routing number, and account number by understanding what they are rather than where they are on the page.
Modern AI-based check OCR handles handwritten fields significantly better than traditional OCR engines. AI vision models are trained on millions of handwriting samples and use contextual clues like the written dollar amount matching the numeric amount to cross-validate extractions. Lido achieves 95 to 99 percent accuracy on handwritten check fields and provides confidence scores so you can route low-confidence extractions to human review.
With template-based tools, each new check format requires 30 minutes to several hours of zone configuration and testing. With AI-powered check OCR like Lido, there is zero setup time per format. You upload the check and the AI extracts fields immediately on the first image. This is the key advantage for teams that receive checks from many different banks or businesses.
Teams that process 500 or more checks per month typically see payback within the first month. Manual check entry takes 2 to 3 minutes per check, so 500 checks consumes roughly 20 hours of staff time. At $25 per hour, that is $500 per month in labor alone, not counting error correction. Lido’s Standard plan starts at $29 per month for 100 pages, and Scale plans handle up to 42,000 pages per year starting at $7,000.
Start free with 50 pages. Upgrade when you’re ready.
Built on Lido’s OCR engine
Built on Lido’s OCR engine
Built on Lido’s OCR engine