AI reads payee names, amounts, dates, MICR lines, and routing numbers from check photos and scans—no manual data entry.
Check image OCR uses AI to extract structured data from photographs and scans of bank checks. The technology reads both printed and handwritten fields—payee name, dollar amount (numeric and written), date, check number, memo line, and the MICR line containing routing number, account number, and check sequence number. Each check becomes a row in a spreadsheet with consistent columns, replacing manual data entry for lockbox operations, accounts receivable teams, and bank back-office processing.
Handwritten payee name ("Pay to the order of") and printed payer information from the check header. AI handles cursive, abbreviations, and business name variations.
Both the numeric amount and the written-out dollar amount are extracted and cross-referenced. The system flags mismatches automatically. Date is parsed into a standard format.
The MICR line is parsed into routing number, account number, and check sequence number. This data drives bank reconciliation and payment matching workflows.
Check sequence number from the top-right corner and memo/reference line are captured for audit trails and payment categorization.
Issuing bank name, branch, and address are read from the check header and correlated with routing number data for verification.
Each extracted field includes a confidence score. Low-confidence values on handwritten fields are flagged for human review rather than accepted silently.
Banks and payment processors receive hundreds of checks daily through lockbox services. Converting check images to structured data eliminates the manual keying step that traditionally bottlenecks the process. A lockbox processing 500 checks per day can extract all fields into a reconciliation-ready spreadsheet in minutes instead of the 4-6 hours required for manual entry.
Companies that still receive check payments from customers need to match each check to an open invoice. Extracting payee, amount, check number, and date into a spreadsheet enables automated matching against the AR aging report. The MICR data provides the bank routing information needed for deposit verification.
Check clearing, exception item handling, and fraud detection all require structured check data. Image-based extraction replaces dedicated MICR reader hardware with software that processes any check image, including mobile deposit captures that may have lower image quality than scanner output.
Auditors reviewing check-based transactions need searchable, structured records. Converting check image archives into spreadsheets makes it possible to filter by date range, amount threshold, payee, or bank. This is critical for SOX compliance, tax audits, and fraud investigations where manual check-by-check review is impractical.
Photograph the check with a smartphone, scan with a flatbed scanner, or export from your remote deposit capture system. JPEG, PNG, TIFF, and PDF formats are all supported.
Drag and drop check images into the platform, or set up automated ingestion via email forwarding, cloud drive sync, or API. Multi-page PDFs containing multiple checks are split automatically.
AI reads every field: payee, payer, amount (numeric + written), date, check number, memo, bank name, routing number, account number, and MICR line. Handwritten fields are cross-referenced for accuracy.
Download as Excel, Google Sheets, CSV, or JSON. Push directly to QuickBooks, Sage, or your ERP via API. Each check becomes one row with consistent columns.
Checks are uniquely difficult for OCR because the most critical data—the payee name and dollar amount—is typically handwritten. Unlike invoices, forms, or bank statements where most text is printed, checks require the AI to interpret cursive handwriting, printing styles, abbreviations, and formatting variations that differ with every writer.
Modern AI vision models trained specifically on check imagery handle this by learning from millions of check samples. The system recognizes common patterns: "Jno." for John, "&" for "and", state abbreviations, and business name conventions. Crucially, the written-out dollar amount ("Three thousand four hundred and 00/100") is cross-referenced against the numeric amount ("$3,400.00") to catch errors. When the two don't match, the extraction is flagged for human review rather than silently accepting either value.
Confidence scoring extends this verification to every field. A clearly printed payee name receives a high confidence score and passes through automatically. A smudged or ambiguous handwritten word receives a lower score and is queued for review. This approach means extraction accuracy on the final output exceeds 99 percent for most operations, because uncertain values are caught rather than guessed.
Yes. Modern AI-powered check OCR reads handwritten dollar amounts, payee names, dates, and memo lines with 93 to 98 percent accuracy. The system cross-references the written amount with the numeric amount printed on the check to catch discrepancies automatically.
Check image OCR extracts payee name, payer name, check amount (numeric and written), date, check number, memo line, bank name, routing number, account number, and the full MICR line. Each field is output as a structured column in Excel, CSV, or JSON.
Check image OCR processes JPEG, PNG, TIFF, PDF, and BMP files. Photos taken with a smartphone camera work as well as flatbed scanner output. For best results, ensure the full check is visible with adequate lighting and minimal skew.
The MICR (Magnetic Ink Character Recognition) line at the bottom of every check contains the routing number, account number, and check number encoded in a special font. AI OCR reads these characters from check images without requiring a magnetic reader, parsing each segment into separate structured fields.
Yes. Batch processing handles hundreds or thousands of check images at once. Upload a folder of scanned checks or a multi-page PDF and receive a single spreadsheet with one row per check. Lockbox operations processing 500+ checks daily typically complete extraction in under 10 minutes.
50 free pages. No credit card required.