AWS Textract vs Open Source OCR for Production Doc AI (Accuracy, Cost, and Architecture)
Key Takeaways
- OCR choice is one part of an end to end extraction system.
- Textract usually wins on time to production and managed scaling.
- Open source wins when you need offline control or have special documents and can invest in ops.
- Add validation and a human in the loop layer to reach production grade accuracy.
- Store raw, normali
Who this is for
If you are building document automation that has to work in production, not just in a notebook, this guide compares AWS Textract with leading open source OCR stacks and shows how we typically architect a reliable, auditable pipeline on AWS with MongoDB as the system of record.
This is written from the perspective of teams shipping: intake, extraction, validation, and downstream workflows (case management, forms, ERPs, CRMs).
The real decision is not "OCR", it is end to end extraction quality
OCR is only one layer. Production outcomes depend on:
- Image and PDF normalization (deskew, denoise, DPI, page splitting)
- Layout understanding (tables, key value pairs, reading order)
- Field mapping to your schema
- Validation, business rules, and confidence gating
- Human in the loop review for low confidence cases
- Auditing, versioning, and traceability
Quick definitions
- OCR: turns pixels into text
- Document understanding: detects structure like tables, forms, and key value pairs
- Extraction: produces structured JSON aligned to your target schema
When AWS Textract is the right default
Textract is often the quickest path to production because it bundles OCR plus higher level primitives.
Use Textract when you need:
- Strong performance on common business docs
- Key value pair extraction for form like PDFs
- Table extraction that preserves row and column semantics
- Managed scaling with minimal ML ops
Authoritative references:
Typical Textract architecture on AWS
- Upload to S3, enforce server side encryption (SSE KMS)
- Trigger Step Functions workflow
- Run Textract (sync for small docs, async for large)
- Post process results into your target schema
- Validate fields with rules and reference data
- Store raw + normalized + extracted outputs with full provenance
Core building blocks:
When open source OCR is the better choice
Open source OCR can be the right move when:
- You must run offline or on prem
- You need full control over the pipeline and models
- Your pages are unusual (handwriting, dense engineering scans) and you plan custom tuning
- Unit economics make managed OCR too expensive at your scale
Common open source options:
Production reality check for open source
Open source OCR is not just a model choice, it creates operational work:
- Container hardening and patch cadence
- GPU decisions and batching strategy
- Quality monitoring, drift, and regression testing
- Layout and table parsing that you may need to assemble yourself
If you want tables, you may end up adding specialized components. For example:
Accuracy strategies that matter more than the OCR engine
1) Preprocessing and page normalization
- Convert PDFs to images at consistent DPI
- Deskew, remove background noise, and enhance contrast
- Detect page orientation and rotate
References:
2) Field level confidence and validation
Treat OCR output as probabilistic:
- Validate dates, phone numbers, SSNs, addresses
- Cross check fields against reference data (client directory, known case IDs)
- Require citations to page and bounding box coordinates for every extracted field
3) Human in the loop for exceptions
A small, fast review UI can raise accuracy dramatically because reviewers correct only what fails validation.
AWS plus MongoDB default architecture
If your default stack is AWS plus MongoDB, we usually recommend:
- S3 for immutable raw document storage
- MongoDB for the structured record, workflow state, and search
- Optional: MongoDB Atlas Search and Vector Search when you add RAG
Authoritative references:
Suggested MongoDB collections
documents: metadata, tenant, status, storage pointerspages: per page preprocessing outputs, OCR blocksextractions: normalized structured JSON, field confidence, citationsreviews: reviewer actions, diffs, timestampsaudit_log: append only events
Security and compliance baseline
If documents contain sensitive data, start with proven baselines:
Cost model: how to compare fairly
Compare by total cost per processed document, not only per page OCR cost:
- OCR cost (Textract or compute)
- Preprocessing compute
- Postprocessing and validation
- Review time
- Storage and retention
- Engineering and maintenance
A cheap OCR engine that causes more review time can be more expensive overall.
zed, and extracted outputs with citations and audit logs.
zation and flexible parsing, but they must be grounded with citations and constrained schemas. Treat them as an assist layer, not the source of truth.
Internal Lid Vizion resources:
FAQs
Q: Is Textract accurate enough for legal and medical documents?
It can be, but success depends on scan quality, preprocessing, and field level validation. For regulated workflows, add strict audit logging, retention policy, and review for low confidence cases.
Q: Can we combine Textract with open source OCR?
Yes. Many teams use Textract for tables and forms, and run a secondary OCR or LLM based extraction for specific fields, then reconcile outputs.
Q: Where does an LLM fit in doc extraction?
LLMs can help with normali
Q: What should we link to internally?
A: Link to relevant solution pages like Computer Vision or Document Intelligence, and only link to published blog URLs on the main domain. Avoid staging links.
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