Case study
Document AI annotation across mixed scripts.
A Document AI team needed a production-ready annotated image dataset across mixed document types, scripts, and structural labels.
~58,000 - Devanagari, Arabic, Latin, and others - Contracts, forms, invoices, and records
110,000+
verified language specialists
300+
languages across active service lines
4,500+
dialects and regional variants
110+
rare and indigenous language pairs
1,000+
brands served since 2015
Measured outcomes
Document AI OCR annotation
Project overview
What landed, and what made it hard.
A Document AI team needed a production-ready annotated image dataset across mixed document types, scripts, and structural labels.
Delivery snapshot
Document AI OCR annotation
- Client
- confidential Document AI buyer
- Service
- OCR annotation and validation
- Volume
- ~58,000 images
- Scripts
- Devanagari, Arabic, Latin, and others
Why this mattered
Outcome before process.
The work combined scanned contracts, handwritten forms, invoices, and medical-style records, which made script literacy and annotation consistency equally important.
The problem to solve
Why the work was difficult, and what MoniSa changed in-flight.
Each image needed annotators who could read the content and apply consistent structural labels across document formats.
The challenge
The problem to solve
Mixed-script files created boundary, OCR, and labeling risks that could not be resolved by generic image annotation alone.
Operating response
What MoniSa changed
MoniSa organized annotators by document type and script, then ran double-validation before senior reviewer escalation.
- Script grouping Files were routed by script and document type before annotation began.
- Double validation A second annotator checked structural labels, text boundaries, and OCR output.
- Senior escalation Disagreements moved to senior review instead of being averaged away.
Results
Measured outcomes from this engagement.
~58,000 images were annotated and validated across multiple document types and script systems.
| Images | ~58,000 |
|---|---|
| Scripts | Devanagari, Arabic, Latin, and others |
| Document types | Contracts, forms, invoices, and records |
| QA model | Double-validation with senior reviewer escalation |
Selection logic
What protected the result.
The selection came down to whether MoniSa could source and review the work at standard, and whether that would hold across the full run.
Why the fit was real
Why the fit was real
The work needed language-aware annotation, beyond bounding boxes or generic labeling.
What decided the result
What decided the result
Script routing and senior escalation kept structure, text boundaries, and OCR checks aligned.
What buyers can reuse
What buyers can reuse
- Document AI work becomes language work when OCR, handwriting, and script boundaries enter the dataset.
- Double-validation reduced the risk of inconsistent labels entering model training data.
- The source client details stay confidential; metrics are scoped to this dataset only.
Continue from this proof
Useful comparisons for the same problem.
Use these links to compare the case with the matching service, buyer guide, and language coverage.
Mapped context
Service and buyer context
Languages named
Examples referenced in the engagement.
- Devanagari
- Arabic
- Latin
- Mixed-script records
More proof
Related proof
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case evidence
Nearest proof pattern.
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Buyer questions
Answers in writing, before you ask for a call.
The questions buyers send before a scope conversation, answered on the page rather than in a meeting. Take them to your team, then send us the one we did not answer.
What was delivered on this engagement?
Images: ~58,000. Scripts: Devanagari, Arabic, Latin, and others. Document types: Contracts, forms, invoices, and records
What control kept the work stable?
Script routing and senior escalation kept structure, text boundaries, and OCR checks aligned.
Where should similar work go next?
Use AI data services for the delivery model, AI data annotation vendor guide for buyer-side evaluation, and the contact page for a scoped brief.
What happens if you cannot staff one of my language pairs?
You are told before a date is agreed, not after. Coverage is reported pair by pair as staffed today or needing a recruitment window, with the window stated — in writing, while the scope is still being agreed. Nobody new goes onto live work until a pilot batch has been reviewed and signed off. A coverage claim you cannot check before signing is not coverage.
Similar brief
Send the constraint behind the metric.
A useful follow-up to a case study names the language mix, review model, deadline, and what proof your buyer team needs before approval.
Production-ready brief
01Closest matching challenge from this case02Language pair, dialect, and script coverage03Volume, cadence, or hours to deliver04Reviewer model and acceptance criteria05Security or platform constraints06Proof needed for stakeholder approval