Trained on check imagery, not built on templates. Extract payee, amount, date, check number, and MICR data from any check format with confidence-scored accuracy.
Drag and drop files, connect a cloud drive, or set up email auto-forwarding. Any file format works—PDF, JPEG, PNG, TIFF, or digital documents.
The AI identifies fields by context and meaning, not fixed coordinates. Names, dates, amounts, and custom fields are extracted automatically.
Get structured output in Excel, Google Sheets, CSV, or JSON. Use the REST API for direct integration into your systems.
“We switched from a template-based OCR tool that needed constant reconfiguration. The AI reads checks from any bank out of the box—including the handwritten ones our old system couldn’t touch.”
“The confidence scores changed our workflow. We auto-approve everything above 97% and only review the 3% that gets flagged. Our throughput tripled without adding headcount.”
“We process rent checks from 400 tenants across 12 properties. Every check is different. The AI handles them all and the accuracy on handwritten amounts is genuinely impressive.”
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.
AI check OCR uses neural network vision models trained specifically on check imagery to identify and extract fields like payee name, date, amount, check number, routing number, and account number. Unlike traditional OCR that relies on fixed templates mapping pixel coordinates to fields, AI check OCR understands the semantic structure of a check—recognizing what each field means rather than just where it sits on the page.
The biggest practical difference between AI and traditional check OCR shows up in two areas: handwriting recognition and multi-format handling. Traditional OCR engines were designed for machine-printed text and perform poorly on cursive handwriting, which is still common on personal and business checks for the payee line, memo, and written dollar amount. AI vision models, trained on diverse handwriting samples, read these fields with dramatically higher accuracy. They also use cross-validation—comparing the written amount (“One thousand two hundred and 00/100”) against the numeric amount ($1,200.00)—to catch errors that a single-field extraction would miss.
Multi-format handling is the other area where AI extraction pulls ahead. Every bank prints checks with slightly different layouts, fonts, security patterns, and field positions. Template-based OCR requires a separate template for each layout, and when a bank updates its check design, the template breaks. Lido’s AI engine reads each check contextually, so it handles checks from any bank on the first upload without configuration. For operations that receive checks from dozens or hundreds of sources, this eliminates a major ongoing maintenance burden.
Confidence scoring is what makes AI check OCR practical at scale. Rather than returning a binary pass-or-fail result, the AI assigns a probability to each extracted value. A payee name extracted with 99 percent confidence can be auto-approved, while a handwritten amount at 82 percent gets routed to a human reviewer. This lets teams build automated workflows that handle the clear majority of checks without human intervention while ensuring that ambiguous extractions still get verified.
Traditional check OCR uses template-based rules that map fixed pixel coordinates to fields. AI check OCR uses vision models that understand the semantic layout of a check, recognizing fields by context rather than position. This means AI check OCR works on any check format from any bank without per-layout configuration, and it handles handwritten fields that template-based systems consistently fail on.
AI check OCR uses neural networks trained on millions of handwriting samples across different writing styles. The model reads cursive and print handwriting contextually, using surrounding text and check structure as validation signals. For example, the written dollar amount is cross-checked against the numeric amount in the courtesy box. Lido reports a per-field confidence score so teams can route low-confidence handwritten extractions to human review.
A confidence score is a percentage value that the AI assigns to each extracted field indicating how certain the model is about the extraction. A confidence score of 98 percent on a payee name means the model is highly certain it read the name correctly. Teams use confidence thresholds to automate high-confidence extractions and flag low-confidence ones for human review, balancing speed with accuracy.
Yes. Because AI check OCR reads checks contextually rather than matching a fixed template, it can handle checks with different layouts, languages, and currency formats. The AI recognizes currency symbols, date formats, and field labels regardless of their position on the check. Lido processes checks from US, Canadian, UK, and international banks without separate configurations.
The underlying AI models are periodically retrained on larger datasets by the provider, which improves accuracy across all users. However, individual account data is never used to train shared models. Lido’s AI engine is updated regularly with improved handwriting recognition and layout understanding, and these improvements apply automatically to all accounts without any action required from users.
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