Read check photos, mobile deposits, and scanned images with AI that handles skew, blur, and poor lighting—no manual cleanup required.
Snap a photo with your phone, use a desktop scanner, or forward check images from your email. The OCR engine accepts JPEG, PNG, TIFF, and PDF—even blurry or skewed captures.
The OCR engine identifies the payee, date, written and numeric amounts, check number, memo line, and MICR-encoded routing and account numbers—regardless of check layout or bank format.
Every extracted field lands in a clean spreadsheet row. Export as Excel, CSV, or JSON, or push check records to your systems via the REST API.
“Our lockbox operation scans thousands of checks daily. Half arrive creased or at odd angles. This reads them all without us having to rescan or manually correct anything.”
“We replaced a legacy check imaging system that required perfect 300 DPI scans. Now tellers can snap a phone photo and the data is extracted in seconds.”
“The image correction built into the OCR is remarkable. Checks photographed on a desk with shadows and glare still extract cleanly on the first pass.”
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.
Check image OCR is the process of reading photographic or scanned images of bank checks and converting the visible information—payee name, dollar amounts, dates, check numbers, memo lines, and MICR-encoded routing data—into structured digital records. Unlike flatbed-scanned documents, check images captured via mobile deposit apps, lockbox cameras, or handheld scanners frequently arrive skewed, shadowed, or at low resolution, making traditional OCR engines unreliable without manual intervention.
The core challenge with check image processing is variability. A single accounts receivable department might receive personal checks printed by dozens of different banks, cashier’s checks with ornate backgrounds, and business checks with custom stub layouts. Each one places fields in slightly different positions with different fonts and security patterns. Template-based OCR systems require a separate configuration for each layout, which becomes unmanageable once you process checks from more than a handful of sources.
AI-powered check image OCR solves this by reading the image contextually. The model identifies the courtesy amount box, legal amount line, date field, and payee line based on their spatial relationships and label text—not fixed pixel coordinates. This means a check photographed at an angle on a desk receives the same extraction accuracy as a perfectly aligned flatbed scan. Lido applies automatic deskewing, contrast normalization, and noise filtering before the AI extraction step, so the input image quality matters far less than it does with legacy systems.
For operations that handle mobile deposit captures, lockbox imaging, or check archival digitization, the practical benefit is eliminating rescan requests and manual data entry corrections. Every image is processed on the first attempt with confidence scores that flag genuinely ambiguous fields rather than failing silently on slightly imperfect captures.
Yes. Modern AI-based check image OCR applies automatic deskewing, noise reduction, and contrast enhancement before extraction. Lido’s engine handles photos taken at angles up to 30 degrees, low-resolution mobile captures, and images with shadows or uneven lighting. The AI adapts to each image individually rather than requiring a clean, flat scan.
Check image OCR extracts the payee name, written and numeric amounts, date, check number, memo line, bank name, MICR line data including routing number and account number, and endorsement information from the back of the check. Lido returns all extracted fields with individual confidence scores so downstream systems know which values to trust and which to flag for review.
Standard document OCR converts text on a page into a string of characters. Check image OCR goes further by understanding check-specific layout conventions such as the courtesy amount box, legal amount line, MICR encoding at the bottom, and signature area. This contextual understanding allows it to correctly label each extracted value rather than returning unstructured text.
Lido’s check image OCR achieves 96 to 99 percent field-level accuracy on standard business and personal checks. For compliance-sensitive workflows, confidence scoring flags any field below a configurable threshold for human review. Combined with SOC 2 Type 2 certification and AES-256 encryption, the platform meets the security and accuracy requirements of financial institutions.
Absolutely. Lido supports batch upload of hundreds or thousands of check images at once through drag-and-drop, cloud drive connections, or email auto-forwarding. Each image is processed independently and results are returned as rows in a single spreadsheet or via the REST API. Scale plans handle up to 360,000 pages per year.
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