Handwritten cheque OCR with 97%+ accuracy on cursive payee names, amounts in words, numeric amounts, and dates — plus MICR reading, cross-field validation, and signature region detection. Chequedb turns cheque OCR output into structured fields with per-field confidence scores, ready for ERP integration, review queues, and approval workflows.
For developer integration, start with the check OCR API page. For ERP integration, see the Chequedb API. For operational review, route extracted fields into the cheque management portal.
Standard OCR vendors work with printed fields only. Chequedb reads the fields that appear on personal cheques — the payee name, numerical amount, legal amount in words, and date — all written by hand. If you process personal cheques, that distinction determines your actual automation rate.
A developer evaluating OCR tools for a cheque processing project will quickly discover that generic OCR libraries — Tesseract, Google Cloud Vision, AWS Textract — cannot handle what cheque operations actually need.
Generic OCR returns free text from an image. Bank check OCR returns structured cheque fields with confidence scores, cross-validation, and a workflow destination. The difference determines whether your team builds one integration or a multi-year in-house replacement for cheque-specific extraction logic.
| Capability | Generic OCR (Tesseract, Google Vision, AWS Textract) | Bank Check OCR (Chequedb) |
|---|---|---|
| MICR line reading | Not supported — MICR font characters return garbled or incorrect values | Magnetic + optical MICR with E-13B and CMC-7 support, routing validation |
| Field localization | Returns all text in reading order; you must build field-location heuristics per cheque layout | Field-specific models locate MICR line, amounts, payee, date, signature, and endorsement regions |
| Handwriting (ICR) | Limited or absent; handwritten payee names and written amounts are typically unreadable | Field-specific ICR models trained on cheque handwriting — 97%+ on amounts, 96.5%+ on payee names |
| Amount cross-validation | Cannot compare courtesy and legal amounts; no CAR/LAR disagreement detection | Automatic comparison of courtesy amount (CAR) and legal amount (LAR); mismatches routed to exception review |
| Date validation | Returns date as text string; no stale/post-dated logic | Configurable stale-dated and post-dated rules per jurisdiction and bank policy |
| Duplicate detection | Not available | Multi-channel duplicate detection using MICR + amount + date comparison across lookback window |
| Confidence scoring | Document-level or character-level; not per cheque field | Per-field confidence scores (0.0–1.0) with configurable auto-accept thresholds |
| Exception routing | Not available — all text values must be handled by your application | Low-confidence fields route to review queues with reason codes, image crops, and recommended actions |
| Audit trail | Not available | Per-event logging: raw OCR read, corrected value, reviewer identity, rule version, and downstream status |
| Image quality checks | May reject poor images but no cheque-specific quality gates | MICR visibility, endorsement region presence, front/back association, skew, blur, and crop checks |
The risk of using generic OCR for cheques: what looks like a cost saving (using a free or per-page OCR tool) becomes an expensive engineering project when you discover it cannot read MICR fonts, it cannot localise handwritten payee fields, it cannot validate amounts against each other, and none of its outputs are structured for the review queues your operations team depends on. The false-accept rate — the system accepting an incorrect read as correct — is the most dangerous metric, and generic OCR tools provide no mechanism to control it. For more detail, see Bank Check OCR: What It Reads, How It Works, and How to Integrate It.
A complete pipeline from image capture to structured cheque data, review, and export
High-resolution image capture (200-300 DPI) via mobile, scanner, or kiosk
Deskew, denoise, binarize, and remove security backgrounds
OCR/ICR recognition for all fields with confidence scoring
Cross-field validation, checksum verification, fraud scoring
Structured JSON output with confidence scores for each field
How Chequedb reads printed fields, handwriting, and MICR in a single extraction pass
Magnetic Ink Character Recognition for the MICR line. Reads routing numbers, account numbers, and cheque serial numbers with near-perfect accuracy.
Optical Character Recognition for printed fields—bank names, addresses, date stamps, and pre-printed account holder information.
Intelligent Character Recognition for handwritten text — payee names, amounts in words, and memo fields. Field-specific models trained on cheque handwriting.
End-to-end neural networks for layout analysis, field localization, and multi-modal fusion across all extraction methods.
Extract every relevant field from cheques with confidence scoring for each element. Our system handles standard layouts, variations, and edge cases.
{
"extraction_id": "ext_20240214120000_abc123",
"confidence": 0.94,
"fields": {
"routing_number": {
"value": "021000021",
"confidence": 0.99,
"source": "micr"
},
"account_number": {
"value": "1234567890",
"confidence": 0.98,
"source": "micr"
},
"amount": {
"numeric": 1500.00,
"written": "One thousand five hundred",
"confidence": 0.89,
"mismatch": false
},
"payee": {
"value": "John Smith",
"confidence": 0.87,
"needs_review": false
},
"date": {
"value": "2024-02-14",
"confidence": 0.95,
"valid": true
}
}
}Submit an image, get structured fields and confidence scores.
curl -X POST \
https://api.chequedb.com/v1/extract \
-H "Authorization: Bearer YOUR_API_KEY" \
-F "image=@cheque.jpg" \
-F "fields=all" \
-F "confidence_threshold=0.85"For real-time processing with immediate response. Ideal for mobile cheque deposit and interactive applications. Average response time under 2 seconds.
For high-volume batch processing. Submit jobs and receive webhook notifications when extraction completes. Handles thousands of cheques per minute.
Native SDKs for iOS, Android, and Web with pre-built UI components. View SDK documentation.
Need the broader deposit workflow view first? Review the bank check OCR API page.
Push extracted cheque fields to SAP, Odoo, QuickBooks, or any custom ERP via REST API. Idempotent posting with control totals, error handling, and full audit trail synchronization. ERP integration docs.
Built for teams that need field-level accuracy, exception control, and review traceability
Every extracted field includes a confidence score (0.0-1.0). Set thresholds for automatic acceptance or manual review routing.
Automatically verify that numeric and written amounts match. Validate routing number checksums and date ranges.
Integrated fraud scoring during extraction. Detects altered amounts, suspicious patterns, and duplicate cheques.
Automatic routing of low-confidence extractions to review queues. Side-by-side image and data comparison interface.
Process thousands of cheques in parallel. Ideal for back-office operations and end-of-day processing.
Sub-second response times for mobile and interactive applications. Webhooks for status updates.
Accuracy measured across 10M+ production cheques including personal, business, and consumer items. Low-confidence handwritten fields route to human review automatically — not posted with uncertain values. How handwriting recognition works.
Enable customers to deposit cheques via mobile apps with instant data extraction and validation. Learn more.
Capture individual cheques or batches with OCR, MICR, duplicate checks, and exception review. Learn more.
Combine extraction with fraud detection to identify altered amounts, forged signatures, and suspicious patterns. Learn more.
Push extracted handwritten cheque data to SAP, Odoo, QuickBooks, or any ERP via REST API with full audit trail. Learn more.
Follow the path that matches your search intent: software, API, portal, or searchable archive.
Developer integration for cheque OCR, MICR, JSON output, and webhooks.
Review extracted fields, manage approvals, resolve exceptions, and audit changes.
Capture cheque images with OCR, MICR, quality checks, and scanner support.
Why 97%+ accuracy on handwritten fields changes what's possible for personal cheque processing.
Browse country-specific landing pages for teams evaluating cheque data extraction workflows in these markets.
Support branch capture, remote intake, shared operations, and lower manual keying across Canadian cheque workflows.
Support bank, fintech, and enterprise operations that need structured cheque data for deposit, validation, and exception handling.
Use one extraction layer for cheque capture, operational review, and downstream handoff into internal finance or banking systems.
Support branch, scanner, and operational cheque intake with validated extraction output and API-ready data delivery.
Support branch capture, operations review, and structured API delivery for cheque workflows moving through Pakistani banking and finance teams.
Support scanned cheque intake, validation, and structured API delivery for banking and finance operations in Kenya.
Book a live walkthrough to see extraction, exception routing, and approvals in context. Then validate your field requirements before moving to sandbox testing.