Please help me choose between OCR APIs for document extraction, with a focus on bounding boxes, tables, and developer experience

If your priorities are bounding boxes, table extraction, and good developer experience, the main OCR/document extraction APIs worth comparing are:

• Google Document AI
• AWS Textract
• Azure AI Document Intelligence
• ABBYY FlexiCapture / Vantage
• Veryfi / Mindee / Nanonets for simpler workflows

Quick recommendation

• Best overall for developer experience + robust document parsing: Google Document AI
• Best for straightforward AWS-native integration: AWS Textract
• Best for Microsoft ecosystem + strong form/table extraction: Azure AI Document Intelligence
• Best for enterprise-heavy accuracy/workflow complexity: ABBYY
• Best for easy setup on common docs/invoices/receipts: Mindee / Veryfi

Side-by-side: what matters to you

1) Bounding boxes

If you need word/line/paragraph coordinates for highlighting, redaction, verification, or downstream layout logic:

• Google Document AI: Strong, rich layout data. Good for pages, blocks, paragraphs, tokens, tables, form fields.
• AWS Textract: Very good. Returns blocks with geometry for lines, words, tables, forms, selection elements.
• Azure AI Document Intelligence: Strong page-level and line/word geometry, plus structured content depending on model.
• ABBYY: Excellent, but often more enterprise-oriented and can feel heavier to integrate.
• Mindee/Veryfi/Nanonets: Usually good enough for common use cases, but less flexible for deep layout needs.

Best for detailed coordinate data: Google Document AI and AWS Textract.

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2) Tables

This is where document APIs differ a lot.

• Google Document AI: Generally very good table structure extraction, especially on complex docs.
• AWS Textract: Strong table detection; widely used and reliable for many scanned docs.
• Azure AI Document Intelligence: Good table extraction, especially in structured business docs.
• ABBYY: Often excellent on complex or noisy tables.
• Niche APIs: Often okay on clean tables, weaker on complex merged cells, rotated tables, or multi-page tables.

Best for tables: ABBYY for quality, Google/AWS for balance of quality + usability.

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3) Developer experience

This usually means:

• quality of SDKs
• clarity of response format
• documentation
• ease of getting started
• ability to fine-tune or choose prebuilt models
• support for async/batch processing
• how painful it is to post-process results

Google Document AI

• Very strong docs and structured outputs
• Good for building pipelines
• Can feel more complex because it is powerful

AWS Textract

• Solid SDKs and easy if you already use AWS
• Response structure is low-level and sometimes verbose
• Good tooling, but you’ll likely write parsing code

Azure AI Document Intelligence

• Good SDK experience
• Prebuilt models are easy to use
• Generally pleasant developer experience, especially in Microsoft environments

ABBYY

• Powerful, but often more enterprise setup and heavier integration
• Developer experience can be less lightweight

Mindee / Veryfi

• Often the easiest to start with
• Simple APIs and focused use cases
• Great if your document types are known and limited

Best DX: Azure and Google, with Mindee/Veryfi easiest for narrow use cases.

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My practical ranking by use case

If you need:

Best bounding boxes + table structure + scalable document understanding

1. Google Document AI
2. AWS Textract
3. Azure AI Document Intelligence

Best enterprise table accuracy

1. ABBYY
2. Google Document AI
3. AWS Textract

Best “ship fast” developer experience

1. Azure AI Document Intelligence
2. Mindee
3. Google Document AI

Best if you are already on a cloud

• AWS → Textract
• GCP → Document AI
• Azure → Document Intelligence

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Important differences to watch

Accuracy is not just OCR

For document extraction, the real question is not “which OCR is best,” but:

• Does it detect the layout correctly?
• Does it preserve reading order?
• Does it output tables with rows/columns?
• Does it provide confidence scores?
• Can you get page, paragraph, word, symbol coordinates?
• How easy is it to correct errors?

Bounding box formats differ

Some APIs return:

• polygons rather than rectangles
• normalized coordinates rather than pixels
• hierarchy: page → block → paragraph → word → symbol

This matters if you need UI highlighting or manual review tools.

Table extraction edge cases

Watch for:

• merged cells
• multi-page tables
• rotated scans
• low-resolution PDFs
• tables without ruling lines
• nested tables

No API is perfect here; you may need post-processing.

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A simple decision guide

Choose Google Document AI if:

• you need strong layout understanding
• bounding boxes and table structure matter a lot
• you want a powerful platform for many document types

Choose AWS Textract if:

• you’re already on AWS
• you want reliable forms/tables extraction
• you’re okay with a slightly more technical output model

Choose Azure AI Document Intelligence if:

• you want a clean developer experience
• you’re in Microsoft/Azure ecosystems
• you want a strong balance of ease and capability

Choose ABBYY if:

• accuracy on complex enterprise documents matters most
• budget and integration complexity are acceptable

Choose Mindee / Veryfi / Nanonets if:

• your document types are limited
• you want quick integration
• you value simplicity over maximum flexibility

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My shortlist for you

If I had to narrow it to 3:

1. Google Document AI — best overall for boxes + tables + structured extraction
2. AWS Textract — great if you want mature table/form extraction and AWS compatibility
3. Azure AI Document Intelligence — best DX balance and quick implementation

If you want, I can also give you:

• a feature comparison table
• a recommendation based on your cloud stack
• or a benchmark/testing checklist to evaluate vendors on your own sample documents.