AWS Amazon Textract
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AI ReadinessAI-Aware (B)63/100
Agent-ready OpenAPI document · curated by JenticAI/MLVisionaws_sig_v425 EndpointsREST
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For Agents
Extract text, forms, tables, IDs, and expense fields from scanned documents and PDFs with structured JSON output and confidence scores.
Use for: I need to extract text from a scanned invoice PDF, Analyze the line items and totals on a receipt image, Find all form key-value pairs in a tax document, Get the structured fields from a driver license scan
Not supported: Does not handle handwriting recognition for non-Latin scripts at GA quality, image labelling, or video analysis - use for document text, form, table, and structured-field extraction only.
Jentic publishes the only available OpenAPI specification for Amazon Textract, keeping it validated and agent-ready. Amazon Textract extracts printed text, handwriting, forms, tables, signatures, and ID and expense fields from scanned documents and PDFs. It returns structured JSON with bounding boxes and confidence scores for each detected element, supporting both synchronous calls for single-page documents and asynchronous jobs for multi-page PDFs stored in Amazon S3. Specialized analyzers cover invoices, receipts, identity documents, and lending packages.
Install Jentic One Beta
Connect the Amazon Textract to your agent
Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the Amazon Textract, or any other public or private API you need. You set the rules, the agent never sees your credentials, and every call is logged.
Two steps, two machines. Install the instance in a safe environment, then register your agent from wherever it runs.
1
Step 1: Jentic One Host machine
# On the machine that will host your Jentic One instance:
curl -fsSL "https://jentic.com/install.sh?src=apis&api=%2Fapis%2Famazonaws.com%2Ftextract" | sh2
Step 2: Agent machine
# On the machine where your agent runs (keep this separate from the instance):
curl -fsSL "https://jentic.com/install.sh?src=apis&api=%2Fapis%2Famazonaws.com%2Ftextract" | sh
jentic register # connects your agent to your Jentic One instanceJentic One is in public beta. The setup above keeps your agent separate from the instance, which is what you want before using real credentials: an agent running as the same OS user as Jentic One can read its stored keys directly. Just evaluating? A single local install is fine to start. See the secure deployment guide for the tiers.
Capabilities
What an agent can do with Amazon Textract API.
Detect printed and handwritten text in single-page images via synchronous DetectDocumentText
Analyze forms, tables, signatures, and key-value pairs through AnalyzeDocument
Run multi-page asynchronous jobs against PDFs stored in S3 with StartDocumentAnalysis
Extract structured fields from invoices and receipts using AnalyzeExpense
Pull identity fields from passports and driver licenses with AnalyzeID
Process complex lending document packages through StartLendingAnalysis
Retrieve job results paginated by JobId with GetDocumentAnalysis and GetDocumentTextDetection
Use Cases
Patterns agents use Amazon Textract API for, with concrete tasks.
★ Invoice and Receipt Data Capture
Accounts payable and expense management workflows use Textract's AnalyzeExpense operation to pull vendor name, invoice number, line items, totals, tax, and payment terms from scanned invoices and receipts. Returned fields include normalized labels and bounding boxes so the calling system can verify against a captured image. Synchronous calls handle single-page receipts; multi-page invoices in S3 use StartExpenseAnalysis with results retrieved via GetExpenseAnalysis.
Call AnalyzeExpense with a scanned invoice PDF, parse the SUMMARY_FIELDS for INVOICE_RECEIPT_ID and TOTAL, and return them as a structured payload
Identity Document Verification
Onboarding and KYC flows use AnalyzeID to extract structured fields from US driver licenses, state IDs, and passports - name, date of birth, document number, expiration, and address. Each field is returned with normalized type, raw value, and confidence score so downstream verification logic can flag low-confidence reads for human review. Operates synchronously on a single image.
Call AnalyzeID with a driver license image, extract the FIRST_NAME, LAST_NAME, and DATE_OF_BIRTH fields, and reject the document if any field has confidence below 90
Multi-Page PDF Form and Table Extraction
Document processing pipelines use StartDocumentAnalysis with FORMS and TABLES feature types against multi-page PDFs in S3. Textract returns hierarchical block JSON - pages, lines, words, key-value pairs, table cells - that downstream code reassembles into structured records. Long jobs notify completion via Amazon SNS so the agent can fetch results once ready.
Start a document analysis job with FeatureTypes [FORMS, TABLES] for s3://docs/loan-app.pdf, poll GetDocumentAnalysis until JobStatus is SUCCEEDED, and return the extracted tables
AI Agent Document Understanding
AI agents use Textract through Jentic to convert unstructured document images into structured JSON they can reason over. Jentic isolates AWS Signature v4 credentials and exposes the synchronous and asynchronous Textract operations as searchable tools, so an agent invoked by 'extract fields from this invoice' calls AnalyzeExpense without ever seeing raw secret keys.
Search Jentic for 'extract text from a document', load the AnalyzeDocument operation, and execute it with the provided document bytes and FeatureTypes [FORMS, TABLES]
Key Endpoints
25 endpoints — jentic publishes the only available openapi specification for amazon textract, keeping it validated and agent-ready.
METHOD
PATH
DESCRIPTION
POST
/#X-Amz-Target=Textract.DetectDocumentText
Detect text in a single-page document synchronously
POST
/#X-Amz-Target=Textract.AnalyzeDocument
Analyze forms, tables, signatures, and queries in a document
POST
/#X-Amz-Target=Textract.AnalyzeExpense
Extract structured fields from invoices and receipts
POST
/#X-Amz-Target=Textract.AnalyzeID
Extract fields from identity documents
POST
/#X-Amz-Target=Textract.StartDocumentAnalysis
Start an asynchronous multi-page document analysis job
POST
/#X-Amz-Target=Textract.GetDocumentAnalysis
Retrieve results of an asynchronous analysis job
POST
/#X-Amz-Target=Textract.StartLendingAnalysis
Process a lending document package
POST
/#X-Amz-Target=Textract.DetectDocumentText
Detect text in a single-page document synchronously
POST
/#X-Amz-Target=Textract.AnalyzeDocument
Analyze forms, tables, signatures, and queries in a document
POST
/#X-Amz-Target=Textract.AnalyzeExpense
Extract structured fields from invoices and receipts
POST
/#X-Amz-Target=Textract.AnalyzeID
Extract fields from identity documents
POST
/#X-Amz-Target=Textract.StartDocumentAnalysis
Start an asynchronous multi-page document analysis job
POST
/#X-Amz-Target=Textract.GetDocumentAnalysis
Retrieve results of an asynchronous analysis job
POST
/#X-Amz-Target=Textract.StartLendingAnalysis
Process a lending document package
Jentic AI Readiness Score
This API is usable in Jentic One now. Its AI-readiness score against Jentic's framework shows where it stands today and where improvements would make it even easier for agents to use.
A
Amazon Textract
- AI-Aware (B)63/100
85
Foundational Compliance63
Developer Experience & Jentic Compatibility44
AI-Readiness & Agent Experience94
Agent Usability50
Security100
AI DiscoverabilityPowered by JenticScoring Framework 1.0.0 | Scoring Engine 0.4.0
Show dimension breakdown
85
Foundational Compliance
Base layer of spec validity and structural soundness.
Grade: ASignals: 4
97%
Lint Results
Aggregated quality score from linter diagnostics, weighted by severity.
100%
Resolution Completeness
Percentage of `$ref` references that resolve successfully.
100%
Specification Validity
Checks whether the API description parses successfully and conforms to its declared specification (e.g., OpenAPI).
42%
Structural Integrity
Structural correctness score based on schema issues using logarithmic dampening.
63
Developer Experience & Jentic Compatibility
Clarity, completeness, and ingestion readiness for developers and tooling.
Grade: B-Signals: 4
0%
Example Density
How richly the API is illustrated with examples.
100%
Example Validity
Percentage of examples that conform to their schemas.
51%
Response Coverage
Percentage of operations with complete response definitions (success, client error, server error).
100%
Tooling Readiness
Health of API ingestion, bundling, and resolution within Jentic pipelines.
44
AI-Readiness & Agent Experience
Semantic breadth, depth, and agent comprehension for AI systems.
Grade: DSignals: 4
24%
Description Coverage
Coverage of descriptions across API elements.
0%
Error Standardization
Coverage of RFC 9457 Problem Details for error responses.
100%
OperationId Quality
Coverage, uniqueness, and casing consistency of operationIds for AI inference.
50%
Summary Coverage
Coverage of summaries across operations/tags/info.
94
Agent Usability
Functional utility, complexity comfort, and AI orchestration readiness.
Grade: A+Signals: 1
94%
Complexity Comfort
Agent comfort level based on API operational and structural complexity.
50
Security
Trust, risk posture, and security compliance.
Grade: C-Signals: 1
50%
Authentication Strength
Average quality of security schemes based on authentication method strength (weakest link for OAuth2).
100
AI Discoverability
Findability, semantic richness, and reasoning readiness.
Grade: A+Signals: 1
100%
Descriptive Richness
Clarity and depth of descriptions across API elements.
Score it yourself
Every API in the directory is allowlisted, so you can re-score it with no key required.
npx @jentic/api-scorecard-cli score <openapi-url>Why Jentic?
What agents get from Jentic-routed access to this vendor.
Setup
Wiring Textract by hand means implementing AWS SigV4 signing, targeting the regional host such as textract.us-east-1.amazonaws.com, and building your own polling loop for asynchronous multi-page jobs plus S3 read plumbing. Through Jentic you install once, import Amazon Textract from the API Directory, store the AWS access key once, and your agent calls it.
Permission scoping
Textract sends the document bytes or S3 location in the request body rather than the URL path, so scoping is by operation, not by path prefix. Limit the agent to the operations it needs, such as AnalyzeDocument and AnalyzeExpense for extraction, and leave others out unless you add them.
Credential isolation
Your AWS access key is stored once, encrypted, by your own Jentic One instance and used to sign each Textract request with SigV4 at execution time. The raw secret never enters the agent's prompt, logs, or context.
Intent-based discovery
Agents search Jentic by intent such as 'extract text from a document' or 'pull fields from an invoice', and Jentic returns the matching Textract operation such as AnalyzeDocument with its input schema so the agent calls the right endpoint without browsing the AWS reference.
Related APIs
Alternatives and complements available in the Jentic catalogue.
FAQs
Specific to using Amazon Textract API through Jentic.
Why is there no official OpenAPI spec for Amazon Textract?
AWS does not publish an OpenAPI specification. Jentic generates and maintains this spec so that AI agents and developers can call Amazon Textract via structured tooling. It is validated against the live API and kept up to date. Get started with Jentic One, the self-hosted execution layer.
What authentication does the Amazon Textract API use?
Textract uses AWS Signature Version 4 with an access key ID and secret access key scoped via IAM policies that grant textract:* and s3:GetObject for the bucket holding source documents. Through Jentic, those credentials sit in the encrypted vault and are signed into each request server-side, so an agent never receives raw long-lived keys.
Can I extract tables and forms with the Amazon Textract API?
Yes. Call AnalyzeDocument synchronously with FeatureTypes [TABLES] or [FORMS] for single-page documents, or StartDocumentAnalysis for multi-page PDFs in S3. Both return BlockType=KEY_VALUE_SET, TABLE, and CELL elements you can walk to reconstruct the structured data.
What are the rate limits for the Amazon Textract API?
Synchronous operations like DetectDocumentText and AnalyzeDocument have per-second transaction limits that vary by region - defaults start around 10 TPS and can be raised via service quotas. Asynchronous jobs are subject to concurrent-job quotas. Check the AWS service quotas console for current values.
How do I run an asynchronous PDF analysis job through Jentic?
Search Jentic for 'extract text from a document' to surface StartDocumentTextDetection and StartDocumentAnalysis. Load the StartDocumentAnalysis schema with the Jentic SDK (pip install jentic), execute it with DocumentLocation pointing to your S3 object, then poll GetDocumentAnalysis with the returned JobId until JobStatus equals SUCCEEDED.
Is Amazon Textract free?
No. Textract charges per page processed, with separate prices for DetectDocumentText, AnalyzeDocument (with FORMS/TABLES/QUERIES), AnalyzeExpense, and AnalyzeID. The AWS Free Tier includes a limited number of pages per month for the first three months. See the AWS pricing page for current per-page rates.
Can I limit what my agent is allowed to do with the Amazon Textract API?
Yes. Because Textract passes the document bytes or S3 location in the request body rather than the URL path, your self-hosted Jentic One instance scopes access by operation, not by path prefix. You decide which operations the agent may call, so you can allow only AnalyzeDocument and AnalyzeExpense for extraction while leaving AnalyzeID, StartDocumentAnalysis, and the rest out unless you explicitly add them. Your own rules also govern which AWS credentials the agent can use to sign those calls.
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