There is no evidence-backed universal winner among the seven document extraction APIs compared here. The right choice depends on whether you need OCR, layout-aware parsing, verified field extraction, cloud-native form processing, or content prepared for retrieval-augmented generation (RAG)—and on how much human review your workflow can tolerate.
The options below are drawn from Sushrut Mishra’s October 6, 2026 comparison for LandingAI. Its product descriptions and reported figures are useful starting points, not independent head-to-head test results. Use them to shortlist candidates, then evaluate your own difficult documents before committing.
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What should a document extraction API actually do?
“Document extraction” can refer to several different jobs. A tool that turns a scanned page into text is not automatically a tool that identifies invoice fields, preserves table structure, or returns evidence for a human to verify a result.
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- OCR: Recognizes text in scanned or image-based documents.
- Layout parsing: Preserves structure such as headings, tables, reading order, and page regions.
- Field extraction: Returns typed values, such as a date or total, rather than only page text.
- Classification and splitting: Identifies document types or separates a combined file into constituent documents.
- RAG ingestion: Produces content and chunks that can be indexed and retrieved by an application.
These capabilities overlap, but they are not interchangeable. First define the output your application consumes; then assess whether a person needs to confirm it and how that confirmation should work.
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- VERSATILE PAPER HANDLING ― Portable scanner scans documents up to 8.5 x 72 in; Also easily digitizes receipts and ID cards to make accounting, bookkeeping, and organizing simpler
- INTUITIVE, HIGH-SPEED SOFTWARE — Epson ScanSmart Software³ is a smart tool allowing you to easily scan, review, and save; Stay organized easily with the help of this Epson scanner
- EASY SETUP — USB-powered connect to your computer for quick and simple scanning; No batteries or external power supply required to operate portable document scanner; Standard Connectivity: USB 2.0
How do the seven options differ?
This table summarizes the October 6, 2026 LandingAI-authored comparison and, where noted, official cloud-provider documentation. Descriptions are not independently tested. “Not stated” means the cited material does not establish a comparable value; it is not a claim that the capability is unavailable.
| API or project | Best-fit task described | Output and grounding described | Deployment or evaluation consideration |
|---|---|---|---|
| LandingAI Agentic Document Extraction (ADE) | Extracting fields that need verification in audit-oriented or regulated workflows. | Markdown and typed JSON, with page, character-range, and bounding-box grounding. | The comparison reports a limit of up to 6,000 pages or 1 GB per document; verify current endpoint and plan conditions. Other deployment details and pricing units are not stated in the comparison. |
| Reducto | Hard documents and regulated teams. | Layout-aware chunks and bounding-box citations. | The comparison reports vendor scale and benchmark claims, but these are not common-corpus results against the other options. Deployment details and pricing units are not stated there. |
| LlamaParse | RAG pipelines already built around LlamaIndex. | RAG-ready Markdown and layout coordinates. | The comparison reports support for 90+ formats; verify the current supported-format list and mode behavior for your files. Deployment details and pricing units are not stated there. |
| Docling | Local, self-hosted, or air-gapped processing. | A unified document representation and Markdown. | The open-source route shifts infrastructure, tuning, and support responsibilities to your team rather than providing hosted-service convenience and an SLA. |
| Unstructured | Ingesting a broad range of file formats. | Typed elements and element-level coordinates. | The comparison notes that complex reading order and tables may need tuning. Distinguish the self-hostable open-source library from hosted-platform features; deployment and pricing depend on which route you assess. |
| AWS Textract | AWS-native managed OCR and form workflows. | Text, tables, forms, and block geometry. | AWS documentation confirms text detection and analysis, not relative accuracy on your corpus. The comparison says downstream semantic structure may require additional work. |
| Google Document AI | Managed extraction in Google Cloud, including specialized document workflows. | Processors for structured extraction, OCR, and specialized documents; Google also describes classification, splitting, custom extraction, and OCR/layout features. | Choose a processor that matches the workflow and validate its pricing against your actual volume. Google says custom extraction can be fine-tuned with as few as 10 documents; that is a Google-described workflow, not a cross-vendor training comparison. |
Which option fits a particular workflow?
For RAG ingestion
LlamaParse is the most directly aligned with a pipeline already using LlamaIndex, according to the comparison. Compare its Markdown and layout coordinates with the chunking and retrieval behavior your application needs. A parser’s output format alone does not establish that retrieved passages will preserve the context your users need.
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For auditable field extraction
ADE and Reducto are presented as options for workflows where extracted information needs to be checked against its source. ADE is described as returning typed JSON with page, character-range, and bounding-box references; Reducto as providing layout-aware chunks and bounding-box citations. Test whether the exact references you receive are granular enough for reviewers to find the source text and whether they remain attached to extracted fields through your pipeline.
For local or air-gapped processing
Docling is the clearest fit among the seven when you need a self-hosted or local path. The Unstructured library is also identified as self-hostable. In either case, weigh control over processing against the engineering work of running, tuning, scaling, and supporting the system yourself.
Rank #3
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- INTUITIVE, HIGH-SPEED SOFTWARE — Quickly scan with this desktop document scanner; Epson ScanSmart Software lets you easily preview scans, email files, upload to the cloud, and more; Plus, automatic file naming saves even more time
- SEAMLESS INTEGRATION — Easily incorporate your data into most document management software with the included TWAIN driver; Office document scanner integrates seamlessly with business workflows
- EASY SHARING — Duplex scanner allows you to scan straight to email or popular cloud storage2 services like Dropbox, Evernote, Google Drive, and OneDrive for simple storage and sharing
- SIMPLE FILE MANAGEMENT — Scanner allows the creation of searchable PDFs with Optical Character Recognition (OCR) and convert scans to editable Word or Excel files effortlessly; Designed for home and office document scanning
For an existing cloud environment
Textract is the AWS-native managed option in this comparison; Document AI is the Google Cloud option. Microsoft now describes Azure Document Intelligence as part of Azure Content Understanding in Foundry Tools. Microsoft positions Document Intelligence’s specialized models for structured documents with common templates, and Content Understanding analyzers for variable-layout, unstructured, and multimodal work. Microsoft says existing production Document Intelligence APIs, endpoints, SDKs, and billing are unchanged. Check the documentation for the specific API version you plan to use: Microsoft identifies preview versions and warns that previews lack an SLA.
For mixed formats or complex layouts
Unstructured is described as supporting broad, multi-format ingestion, while the comparison reports 90+ formats for LlamaParse. Those counts do not establish equal handling quality across formats. Test the particular file types and layout patterns that matter to you, especially tables, multi-column pages, and documents whose reading order is not obvious.
Rank #4
- Scanner type: Document
- Connectivity technology: USB
- With Auto Scan Mode, the scanner automatically detects what you're scanning
- Digitize documents and images
What do the published numbers establish?
The reported figures are attributed claims, not comparable results from one evaluation. Mishra’s October 6, 2026 comparison for LandingAI reports that Reducto had processed more than 4 billion pages and achieved 99.6% precision and recall over 225 long documents on LongExtractBench. The comparison does not provide a common test across all seven APIs, so those figures cannot show that Reducto outperforms the alternatives on your files.
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The same comparison reports 90+ supported formats for LlamaParse and up to 6,000 pages or 1 GB per document for ADE. Confirm the current format list and applicable limits with the provider before designing around them. AWS’s Textract API reference was last published August 27, 2026; it confirms the service’s text-detection and analysis scope, not relative accuracy. Google’s 10-document custom-extraction figure and Microsoft’s five-example training figure describe different vendors’ workflows and conditions, so they should not be treated as a direct comparison of training efficiency.
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- AVOIDS PAPER JAMS AND DAMAGE. Features a brake roller system to feed paper smoothly, a multi-feed sensor that detects pages stuck together, and skew detection to prevent paper damage and data loss
How should you compare finalists on your own documents?
Shortlist two or three tools and run the same representative files through each. Include ordinary documents as well as the cases most likely to break your workflow: poor scans, unusual reading order, multi-page tables, mixed document types, and the languages and layouts you actually receive.
- Define the expected result. Specify the fields, tables, text, classifications, or RAG chunks your application requires. Create a reference set with correct answers and source locations.
- Check extraction correctness. Measure field-level errors and missing values against the reference set. Separate OCR mistakes from errors in interpreting or structuring correctly recognized text.
- Inspect structure and tables. Check reading order, row and column relationships, multi-page table continuity, and whether headings stay associated with the right content.
- Verify source grounding. For values a person must review, test whether the output gives usable page locations or bounding regions and whether the reference can be followed back to the original document.
- Measure workflow performance. Record latency and failure handling under realistic conditions, including how the system behaves when a file is unsupported, ambiguous, or low quality.
- Check deployment and data handling. Confirm that the available hosting or self-hosting model, integration requirements, and data-handling terms meet your operational and compliance needs.
- Calculate full workflow cost. Include the provider’s current pricing unit, expected document volume, infrastructure and maintenance for self-hosted tools, and the staff time required to review or correct results. The comparison does not provide a common price basis for all seven.
Track these as separate outcomes rather than collapsing them into one accuracy score. A tool can recognize the text correctly but still put a number in the wrong field, lose a table relationship, or provide evidence that is too coarse for a reviewer.
When can extracted output be trusted without rereading every page?
That depends on the cost of an error and the evidence your system returns—not on the API’s label or a single vendor-reported score. Sushrut Mishra, writing for LandingAI, frames the selection question as whether output can be trusted enough to act on without someone rereading the page. For a real workflow, turn that into a policy: define which fields may pass automatically, what confidence or validation checks they must meet, and which cases go to human review.
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