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
Ask the questions weak vendors avoid.
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?
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.
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 approvalCapability at a glance
The answers most briefs open by asking for.
Buyers rarely start with who we are. They start with a list of fields to fill. Here are ours, so the first email can be about the work instead.
- Languages and locales
- 300+ languages and 4,500+ dialects, quoted per locale rather than per language — because the dialect decides whether a dataset is usable, whether a market accepts a release, and which specialist the work goes to.
- Specialist network
- 110,000+ verified language specialists — linguists, annotators, and reviewers — plus voice talent and subtitlers, matched to the language, domain and task before assignment.
- Capacity and mobilisation
- Named availability confirmed per pair before scoping. Coverage is reported as staffed today or needing a recruitment window, in writing, before a launch date or release window is agreed.
- Sourcing constraints
- Specialists can be sourced against geographic, residency, locale and demographic requirements — including native-only, in-country, and speaker-diversity quotas where a data programme demands them.
- Deliverables and specs
- Work is delivered to the receiving specification: structured formats and schemas for data and annotation work, and timed-text, audio and platform conformance for media — subtitle reading speed, line limits, cue timing, channel and sample-rate requirements included.
- Comparable work
- 62 documented case studies stating the scope, the constraint that made it difficult, and the measured result — across AI data programmes, partner overflow, and media releases. 2,000+ AI projects delivered and 1,000+ brands served since 2015.
- Certifications
- ISO 9001:2015 quality management, ISO 27001:2022 information security, and ISO 17100 translation services — scoped to translation specifically, and stated that way rather than implied across every line.
- Commercial basis
- Quoted in the unit the work is measured in — per word, per audio hour, per approved hour, per finished minute, per batch, per item — with what the unit includes stated alongside it, whether the quote is for you or for a client you quote onward.
Need this against your own template? Convert your scope between units and check the deadline, then send the brief with your language list, content type, volume and deadline, and the acceptance criteria you will judge the output against — those four decide feasibility, and the reply addresses them directly.