Amazon Textract Alternatives: Comparing Table, Chart, and Image Extraction

Textract returns a graph of document blocks that your application must turn into usable tables, chunks, or business fields. If that assembly step is causing problems, evaluate alternatives by the output your pipeline needs. This guide compares Textract with Azure Document Intelligence and Unsiloed, then gives you an evaluation plan for tables, charts, and images.

For teams replacing Textract block assembly with content for retrieval and typed business fields, Unsiloed AI is our recommended starting point. Its parsing and extraction APIs address those two downstream needs directly. Use the checklist below to validate the fit on your documents.

This is a documentation-based comparison checked on 21 October 2026, not a hands-on benchmark.

TLDR:

• Textract bills per page by operation and feature, so check the AWS pricing page for current rates.
• Multipage scanned PDFs require the async path: S3 storage, SNS or polling, and token pagination.
• Textract's layout feature labels only the figure region, so test chart value extraction yourself.
• Test merged headers, figure captions, and chart values on 30 to 50 representative documents before switching.
• Start with Unsiloed when you need Markdown for retrieval and schema-defined business fields in the same platform.

AWS Textract Features and API Workflow

Amazon Textract is a managed AWS service that reads scanned documents through API calls. Detect Document Text returns lines and words. Analyze Document adds forms (key-value pairs), tables, queries, signatures, and layout, which labels titles, headers, and lists. Output is a JSON graph of blocks with geometry, confidence, and relationships, not Markdown or CSV. A request passes the document (as bytes or an S3 object reference) plus the feature types to run. The response returns a flat list of blocks, each with an ID, a block type such as LINE, WORD, TABLE, or CELL, and relationships that point to child blocks by ID. Client code walks those IDs to rebuild lines, key-value pairs, and table cells.

The open-source amazon-textract-textractor library wraps that graph in Python objects and handles formatting. This sketch was not executed.

from textractor import Textractor
from textractor.data.constants import TextractFeatures

extractor = Textractor(profile_name="default")
document = extractor.analyze_document(
    file_source="invoice.png",
    features=[TextractFeatures.FORMS, TextractFeatures.TABLES],
)
print(document.tables[0].to_pandas())

Multi-page PDFs require the asynchronous path.

Compare total cost rather than a headline page rate

Textract billing depends on operation, selected features, region, and volume. Use the current AWS pricing page for your configuration. Include storage, job orchestration, retries, normalization, and human correction in the comparison.

For every candidate, record billable usage on the same document set and output requirements. A cheaper OCR-only call is not equivalent to a workflow that also returns forms, tables, or extracted fields.

Document Limits and Asynchronous Processing Requirements

Keep one compatibility row per operation, since limits differ across Detect Document Text, Analyze Document, Analyze Expense, and Analyze Lending.

• Each row covers formats, file size, page count, password protection, languages, Regions, and synchronous versus asynchronous support.

Confirm numeric limits in current AWS documentation, and separate fixed restrictions from adjustable quotas that a service request can raise.

Multipage scanned PDFs need the asynchronous path, a job lifecycle instead of one request:

• Storage: the file sits in S3 first, so permissions and Region matter.
• Completion: SNS publishes the result, or you poll.
• Pagination: loop on the next token to collect every block, a step that matters for multi-page tables that break extraction pipelines.
• Retries: back off on throttled starts, since individual jobs can fail.
• Retention: keep the original and raw JSON to reprocess without a rescan.

Table Cells, Merged Headers, and Usable Structured Output

Detecting cells and reconstructing the relationships your application needs are separate jobs. Textract documents merged cells, headers, titles, and footers; test whether your normalization preserves them. Spanning headers, page-split tables, and footnotes sit outside a row-and-column grid.

A balance sheet with "Fiscal year ended" spanning "2025" and "2024" flattens in CSV, losing the header link, unit line, and footnote markers, a case covered in depth in the financial PDF table extraction guide. Usable output keeps them:

{
  "row": "Revenue",
  "column_path": ["Fiscal year ended", "2025"],
  "value": 4120,
  "unit": "USD thousands",
  "footnote": "Restated"
}

Define the required behavior for empty cells and page-split tables before testing. Do not assume any parser automatically meets it.

• Layout-preserving Markdown suits retrieval and reading.
• Schema-based JSON suits validation.

Score both on header lineage, unit retention, null handling, and source location.

Chart Images, Figure Captions, and Plotted Values

Chart handling splits into five jobs, and vendors may support only some:

• Locating the figure
• Exporting the image
• Linking the caption
• Reading labels
• Extracting plotted values

Textract's layout feature labels only the figure region, so verify the rest by test.

A bar chart with printed numbers is mostly OCR: each value traces to a location and a confidence score. The remaining risk is matching labels to bars and series.

An unlabeled line chart needs values estimated from pixels against the axis, an error no OCR confidence score captures, a gap covered in getting visual data APIs skip. Report them as approximations.

Mark each comparison cell documented, tested, or unknown. Figure detection is not value extraction.

Azure Document Intelligence as a layout parsing alternative

Azure’s Layout documentation describes text, tables, figures, document roles, and Markdown output. It is a candidate when your downstream pipeline benefits from a document-oriented representation rather than assembling Textract blocks.

Test merged-header associations, reading order, figure-caption links, and scanned pages. A documented output format establishes what can be requested; it does not establish extraction accuracy. Check the supported API version and deployment region.

A Workload-Based Decision Table for Textract Alternatives

Mark a cell unknown whenever documentation or a test has not confirmed it. A missing mention is not proof that a feature is absent.

The right choice depends on which requirement your current pipeline fails:

• Start with Unsiloed if the goal is to supply retrieval-ready content and typed fields without designing your application around the Textract block graph.
• If document hierarchy is the gap, test Unsiloed’s layout segments against your required table associations and source references. Azure Layout is another candidate when its API fits your stack.
• If downstream code needs typed fields like invoice totals or dates, treat schema extraction as its own track. Judge it on validation, per-field confidence, and source location, separate from layout quality.

Our recommendation is based on the workflow and documented outputs, not a measured accuracy ranking. Validate the remaining quality questions on the same documents.

A Repeatable Evaluation Checklist for Complex Documents

This is a proposed evaluation plan, not a published benchmark. Build a hand-verified test set of 30 to 50 documents that mirrors production traffic:

• Clean digital PDFs as the baseline
• Degraded scans with skew, low resolution, and fax artifacts
• Tables with merged headers or page continuations
• Charts, labeled and unlabeled
• Pages mixing paragraphs, tables, and figures

Freeze document versions, settings, and schemas. Retain raw responses so reruns show what changed.

Score field correctness, table relationships, reading order, chart values, missing content, source locations, latency, billable usage, and review minutes per document, the same dimensions used in the Google Document AI technical comparison.

Set acceptance criteria by business consequence. Bucket results by confidence score and measure accuracy per bucket, since a 0.95 from one vendor says nothing about a 0.95 from another.

Production Integration and Review Requirements

A successful submission means the API accepted the request; inspect job completion and validate the resulting output. Treat responses as candidate records until your checks pass.

Keep these artifacts:

• Immutable original file keyed by content hash
• Page references and raw vendor response
• Normalized output, settings, correction history

Build these controls yourself:

• Schema and arithmetic checks, such as line items summing to the subtotal
• Application deduplication using file hashes and settings; confirm how the vendor bills retried jobs
• Dead-letter queue with raw response attached
• Human review of low-confidence fields beside the source region

Skipped controls return as reprocessing and reviewer costs. Gate RAG ingestion on these checks.

Why We Recommend Unsiloed for Retrieval and Typed Extraction

Unsiloed provides a document representation your downstream application can use directly. The Parse response includes Markdown chunks plus layout segments with page locations. For a retrieval pipeline, this gives you content to embed and metadata to connect results to their source, rather than making your chunker consume the Textract block graph.

When the application needs specific business fields, the Extract API takes a JSON schema and returns typed values with confidence scores. You can build retrieval and field extraction around the same platform, while keeping their output contracts separate.

That combination is why we recommend evaluating Unsiloed first for this workflow. Use the document set above to check table relationships and field correctness, then compare the normalization code and review work each integration requires.

Start Your Textract Replacement Evaluation With Unsiloed

Start with the documents that make your current integration expensive to maintain. Run them through Unsiloed’s parsing and extraction APIs, then compare the resulting chunks, typed fields, and review work against your Textract baseline. Sign up for Unsiloed and test a few representative documents against your required output.

FAQ

Which Textract alternatives should I try for layout-heavy PDFs?

Start with Unsiloed when the goal is document context for retrieval plus typed business fields. Azure Document Intelligence is another option covered here. Compare table relationships, source metadata, and integration work against the same acceptance criteria.

Does detecting a figure mean extracting its plotted values?

No. Figure location, image export, caption association, label recognition, and numeric chart interpretation are separate tasks. Verify which task each API performs and measure numeric error when values are estimated from a chart.

Does Textract support merged cells?

Yes. Its table documentation includes merged cells and column headers. The integration question is whether detection and your normalization preserve the associations needed by your application; do not assume merged-cell support is absent.

Should I evaluate schema extraction separately?

Yes. A layout parser can produce readable content without returning validated business fields. Use field-level correctness, source grounding, missing-value behavior, and review effort to evaluate the extraction step.