For scanned archives with search indexing, the best OCR API depends on whether you need highest text accuracy, layout preservation, handwriting support, or bulk-scale economics. In most archive/search workflows, I’d shortlist these:

Best overall for scanned archives: Google Cloud Vision / Document AI

Why it’s a strong choice

• Very good OCR accuracy on clean and moderately complex scans
• Strong support for printed text, page layout, and document structure
• Works well for building searchable indexes from PDFs/TIFFs/images
• Good ecosystem if you also want classification, extraction, and pipeline automation

Use it if

• You need reliable OCR at scale
• You care about text extraction plus structure
• Your archive is mostly printed documents

Best for enterprise document processing: Azure AI Document Intelligence

Why

• Strong OCR plus document layout extraction
• Good for searchable archives and downstream entity extraction
• Useful if you already use Microsoft/Azure stack
• Handles many document types well

Use it if

• You want OCR + searchable indexing + document understanding in one service
• You’re already on Azure
• You need forms/tables/layout along with text

Best for AWS-centric workflows: Amazon Textract

Why

• Good OCR and layout extraction
• Integrates nicely with AWS storage/search pipelines
• Solid for documents with tables/forms

Use it if

• Your archive is already in S3/AWS
• You want easy integration with Lambda, Step Functions, OpenSearch, etc.

Best for maximum OCR accuracy on historical or messy scans: ABBYY FineReader Engine / ABBYY Vantage

Why

• Often among the best for challenging scans, skewed pages, degraded documents, mixed layouts
• Strong reputation in archival and enterprise document digitization
• Better than many hyperscalers on difficult scans

Use it if

• Your archive has poor-quality scans, old newspapers, faded text, odd layouts
• Accuracy matters more than cloud simplicity or cost

Best open-source option: Tesseract OCR

Why

• Free and self-hostable
• Good enough for many clean printed documents
• Useful if you need full control or on-prem deployment

Use it if

• Budget is tight
• You need on-prem/privacy
• Your scans are reasonably clean

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My practical recommendation

If your goal is search indexing for scanned archives, I’d choose:

• ABBYY if the archive is old, messy, or OCR quality is critical
• Google Document AI if you want the best all-around cloud option
• Azure Document Intelligence if you want a strong enterprise pipeline
• Tesseract only if you need open-source/on-prem and can accept lower quality

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For search indexing specifically, look for these OCR features

Make sure the API returns:

• Word-level or line-level text
• Bounding boxes / coordinates
• Confidence scores
• Page segmentation or layout info
• PDF output with text layer or easy downstream indexing

This makes it much easier to build:

• full-text search
• snippet highlighting
• page jump-to-result
• filtering by confidence or page

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Simple rule of thumb

• Best accuracy on archival scans: ABBYY
• Best cloud general-purpose OCR: Google Document AI
• Best Microsoft integration: Azure Document Intelligence
• Best AWS integration: Textract
• Best free/self-hosted: Tesseract

If you tell me:

1. your archive type (books, newspapers, forms, manuscripts),
2. scan quality,
3. volume, and
4. cloud vs on-prem constraints,

I can recommend the best specific API and indexing architecture.