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Industry solution

AI training data for Document AI / OCR

Document AI annotation covers invoice, contract, form, and receipt labeling for OCR, intelligent document processing, and enterprise content AI.

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Overview

Data operations tuned to Document AI / OCR

Enterprises across legal, finance, insurance, and healthcare are training document AI models that must extract structured data from unstructured text, tables, forms, and scanned images. Corpshore AI delivers annotation for document layout analysis, key-value extraction, named-entity recognition, and handwriting transcription with annotators who understand the document domain, not just the pixel.

Invoice, receipt, and purchase order annotation for IDP pipelinesContract clause labeling and legal-document structure annotationForm digitization and handwriting transcription datasetsNamed entity and relationship extraction for enterprise NLP
AI training data for Document AI / OCR at Corpshore AI
The challenge

Where the data breaks down

  • Document layouts vary enormously across templates, languages, and scan quality, so annotation ontologies must be designed to generalize.
  • Tables, multi-column layouts, and rotated text require annotators who understand document structure, not just image bounding boxes.
  • Low-resource-language documents (Arabic, Uzbek, Thai) need native-script annotators to catch reading-order and character-level errors.
  • High-volume IDP pipelines require consistent throughput with accuracy SLAs, which crowd-based annotation cannot sustain.
Our approach

How Corpshore AI delivers

  • We design the annotation ontology with your document types first, then build gold sets and calibration packs before production begins.
  • Annotators are trained on layout-aware labeling: tables as row-column structures, reading order preserved, field relationships marked.
  • Native-script annotators handle Arabic, Uzbek, Hindi, Chinese, and other non-Latin documents so OCR errors and reading-order issues are caught.
  • A three-tier QA cascade reviews every document against the ontology before delivery, flagging layout ambiguities for senior review.
Delivery workflow

From scope to delivery, in document ai / ocr

Step through how a document ai / ocr engagement runs under one operator and a single SLA.

1. Scope

We map your document types, fields, and ontology, then build gold sets and calibration packs.

Stage 1 of 5
Data types

What we deliver

Bounding-box and polygon annotations for document regions and fields
Key-value extraction labels (field name, field value, data type)
Table structure annotation (row, column, cell, header, span)
Named entity labels (organization, person, date, amount, location)
Transcription of handwritten and printed text across scripts
97%+
field-extraction accuracy via three-tier QA
35+
languages including Arabic, Hindi, Chinese, and Uzbek
50 to 70%
cost advantage versus US-domestic document annotation
Single SLA
from document intake through delivery
Why Corpshore AI

The operator advantage for document ai / ocr

  • Native-script annotators across 35+ languages mean Arabic and Hindi document AI gets real linguistic accuracy, not an English-first overlay.
  • One operator owns the full pipeline from annotation through QA, so field-extraction accuracy has a single accountable owner.
  • Domain-aware calibration built into the process — not bolted on as a QA afterthought — keeps accuracy consistent as volume scales.
Relevant services

Services behind this solution

Data CollectionField & physical-world dataAnnotation & LabelingEvery modality, 97%+ accuracyAI ConsultingStrategy, analysis & delivery
FAQ

Document AI / OCR, answered

Corpshore annotates invoices, contracts, forms, receipts, purchase orders, medical records, government IDs, and general scanned documents across print and handwriting.

Ready to scope a pilot?

Tell us your modality, volume, and languages. We'll return an indicative scope, timeline, and cost band.

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