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
Language specialist network
300+
languages across active service lines
4,500+
dialects and regional variants
110+
rare and indigenous language pairs
1,000+
projects delivered 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
Each image needed annotators who could read the content and apply consistent structural labels across document formats.
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 groupingFiles were routed by script and document type before annotation began.
- Double validationA second annotator checked structural labels, text boundaries, and OCR output.
- Senior escalationDisagreements 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 work needed language-aware annotation, beyond bounding boxes or generic labeling.
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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Nearest proof pattern.
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Buyer questions
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Short answers for buyers checking fit, coverage, quality method, and next-step readiness.
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?
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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