Vision Extract : Finance Document OCR

Vision Extract accepts uploaded finance documents : invoices, receipts, payment slips, tax invoices : and returns a canonical document representation as structured JSON, full text, or markdown. A Next.js review UI lets operators verify and correct extracted fields before they go to accounting.

Raw OCR output is not enough for finance workflows. You need reliable fields (invoice number, date, amount, vendor), audit history, and a way for humans to catch mistakes before data enters the accounting system.

Reduce manual data entry from finance documents while making every extracted value reviewable and auditable.

• Upload endpoint receives image or PDF files.
• PaddleOCR PP-OCRv5 pipeline extracts text and bounding boxes.
• Post-processing layer maps raw OCR output to structured finance fields.
• Next.js review dashboard shows extracted fields with confidence indicators.

• Upload queue with processing states.
• Structured field extraction : invoice number, date, vendor, amount, tax.
• Confidence indicators and side-by-side correction UI.
• Export to structured JSON or CSV.

• PaddleOCR PP-OCRv5 mobile detection and recognition models.
• Layout-aware post-processing for finance document structures.
• Optional LLM cleanup for ambiguous vendor names or descriptions.

1. 1User uploads document via web UI or API.
2. 2Backend stores file metadata and pushes a processing job.
3. 3OCR worker runs detection → recognition → post-processing.
4. 4Validation rules flag missing or suspicious fields.
5. 5Operator reviews and corrects fields through the dashboard.
6. 6Corrected data is exported to the target system.

• Different document layouts across vendors and document types.
• Handling poor scan quality, rotated images, and mixed languages.
• Avoiding silent extraction mistakes on financial values.

• Prioritize deterministic field validation over LLM guesswork for totals.
• Keep human review mandatory for financial exports.
• Store original OCR text alongside corrected values for audit trail.

Working pipeline with FastAPI backend and Next.js review UI. OCR accuracy metrics depend on document quality and require labeled test samples for precise measurement.

• Add table extraction for line-item details on invoices.
• Add per-vendor templates for high-volume document sources.
• Build an accuracy benchmark from human-corrected samples.