Image / OCR review

Every pixel, every field, verified

A walkthrough of the reviewer interface — the split view, the per-field confidence scoring, the correction log, and how every reviewer edit becomes a training signal for your model.

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Review interface

What reviewers see

Every extracted field is compared against the source image. Reviewers see confidence scores, match status, and can correct mismatches with inline annotations — all feeding back into model training.

INV-2026-04821 2 corrections
Task #4821 · Invoice processing · Reviewer: doc-specialist-7
Source image invoice_scan_04821.jpg
Overall confidence
78%
Extracted fields AI extraction v3.2
Invoice #
Match 98%
INV-2026-04821
Date
Match 99%
2026-03-15
Vendor
Mismatch 82%
AI: Acme Corp.
Correct:
Acme Corp (no period)
Reviewer note:

Source image shows "Acme Corp" (no period). AI extraction added trailing period. Minor but will cause matching failures in ERP import.

Total
Mismatch 64%
AI: $4,280.00
Correct:
$4,280.00 (value correct, confidence low)
Reviewer note:

Fold mark partially obscures the "8" in source. Verified correct by line-item sum: $3,200 + $480 + $600 = $4,280. Extraction accurate but confidence penalised by image quality.

PO Number
Match 97%
PO-88421
Line Items
Match 95%
3 items extracted Platform license — $3,200 Support plan — $480 Training session — $600
Verdict
Pass Corrections needed Fail
Reviewer: doc-specialist-7 · 4 years document processing · 6,800+ OCR reviews · 96% accuracy
Capabilities

Built for extraction accuracy

Field-level confidence scores

Every extracted field gets a per-field confidence score. Reviewers see at a glance which fields the model is uncertain about — no guessing which parts need the most scrutiny.

Confidence breakdown
Invoice #: 98% — high confidence, clean scan
Date: 99% — clear text, no ambiguity
Vendor: 82% — trailing period added
Total: 64% — fold mark obscures digit

Side-by-side source comparison

Source image and extracted data displayed in a split view. Reviewers compare every field against the original document without switching tabs or losing context.

Review layout
Left panel: original scan / photo
Right panel: all extracted fields
Mismatch fields highlighted inline
Confidence bar per field

Correction tracking for model training

Every correction a reviewer makes is logged with the original extraction, the corrected value, and the reason. This data feeds directly back into model fine-tuning pipelines.

Training data format
Original extraction captured
Corrected value + rationale
Confidence delta recorded
Exported as structured JSON

Anomaly and edge case flagging

Reviewers annotate not just errors but unusual patterns — obscured text, unusual layouts, unexpected formats. These edge cases become the most valuable training signals.

Flag categories
Image quality issues
Unusual document format
Field extraction ambiguity
Cross-field validation failure
Where it fits

Six document types, one review interface

Most requested
Invoice processing

Validate AI-extracted invoice fields against source images for accounts payable automation and ERP integration.

ID verification

KYC compliance workflows.

Receipts

Expense management.

Forms

Applications, registrations.

Insurance claims

Claims processing accuracy.

Multilingual

Mixed-language documents.

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