Install Jentic One Beta

Connect the Auto-Reader OCR + Translate API to your agent

Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the Auto-Reader OCR + Translate API, 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.

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%2Fauto-reader.com%2Fauto-reader" | sh

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%2Fauto-reader.com%2Fauto-reader" | sh
jentic register       # connects your agent to your Jentic One instance

Jentic 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 Auto-Reader OCR + Translate API.

Run multilingual OCR on images, including Arabic-first and manga-aware recognition

Detect and extract text regions from an image before recognition

Translate supplied text, or run OCR and translation together in one call

Process a full document or submit a long book as an asynchronous job

GET STARTED

Start building with Auto-Reader OCR + Translate API

Explore with Jentic One

View OpenAPI Document

Use Cases

Patterns agents use Auto-Reader OCR + Translate API for, with concrete tasks.

★ Scanned Document Extraction

An agent sends a scanned page to Auto-Reader, extracts the text regions, and runs OCR to turn the image into structured text it can act on. The Arabic-first model handles right-to-left scripts, so the agent processes multilingual scans without a separate recognizer per language.

Call POST /v1/detect to find text regions, then POST /v1/ocr to extract the text from the image

OCR and Translate in One Pass

An agent takes a manga page or a foreign-language scan and returns readable translated text by calling the combined OCR-and-translate endpoint. Auto-Reader recognizes the source text and translates it in a single request, and a glossary keeps recurring names consistent, so the agent delivers a translated page without chaining two services.

Call POST /v1/ocr-translate with the page image to extract and translate its text in one request

AI Agent Integration via Jentic

An AI agent reaches the Auto-Reader API through Jentic without holding the bearer key itself. It searches by intent, receives the matching operation with its input schema, and executes the call with the credential injected at run time, so OCR and translation become steps in a larger document workflow.

Search Jentic for 'extract text from an image', load the POST /v1/ocr schema, and execute it with Jentic-managed credentials

Key Endpoints

31 endpoints — the auto-reader ocr + translate api reads text from images and documents and translates it, with an arabic-first and manga-aware recognition model.

METHOD

PATH

DESCRIPTION

POST

/v1/ocr

Run OCR on an image

POST

/v1/manga

Run manga-aware OCR on a page

POST

/v1/ocr-translate

Run OCR and translation in one call

POST

/v1/translate

Translate supplied text

POST

/v1/document

Process a document

POST

/v1/book

Submit a book job

GET

/v1/book/{job_id}

Check a book job status

PUT

/v1/glossary

Set a translation glossary

POST

/v1/ocr

Run OCR on an image

POST

/v1/manga

Run manga-aware OCR on a page

POST

/v1/ocr-translate

Run OCR and translation in one call

POST

/v1/translate

Translate supplied text

POST

/v1/document

Process a document

POST

/v1/book

Submit a book job

GET

/v1/book/{job_id}

Check a book job status

PUT

/v1/glossary

Set a translation glossary

Why Jentic?

What agents get from Jentic-routed access to this vendor.

Setup

Wiring Auto-Reader by hand means managing its bearer key, choosing multipart or base64 upload for each image, and polling book and document jobs yourself. Through Jentic you install once, import Auto-Reader from the API Directory, store the key once, and your agent calls it.

Permission scoping

Auto-Reader carries the image or text inside each request body, so a rule bounds which operations your agent may call rather than which file it reads. You choose the operations it may use, such as OCR and translation, so the data-deletion operation is not included unless you add it.

Credential isolation

Your Auto-Reader key is stored once, encrypted, by your own Jentic One instance and injected at execution time. It never enters the agent's prompt, logs, or context.

Intent-based discovery

Agents search Jentic by intent such as 'extract text from an image' or 'translate a document', and Jentic returns the matching Auto-Reader operation with its input schema so the agent calls the right endpoint without browsing the reference docs.

FAQs

Specific to using Auto-Reader OCR + Translate API through Jentic.

What authentication does the Auto-Reader OCR + Translate API use?

The Auto-Reader API authenticates with a bearer key in the Authorization header, per its OpenAPI spec. Through Jentic the key is stored encrypted by your own Jentic One instance and injected at execution time, so it never reaches the agent.

Can I run OCR and translation in one call with the Auto-Reader API?

Yes. POST /v1/ocr-translate extracts text from an image and translates it in a single request, while POST /v1/ocr and POST /v1/translate run each step on its own. A glossary set via PUT /v1/glossary keeps recurring terms and names consistent across a job.

What are the rate limits for the Auto-Reader API?

The Auto-Reader OpenAPI spec does not specify rate limits. Watch for HTTP 429 responses and check Auto-Reader's documentation for the current limits, and run long inputs as book or document jobs rather than many synchronous calls.

How do I extract text from an image with the Auto-Reader API through Jentic?

Search Jentic for 'extract text from an image', and it returns the Auto-Reader operation with its input schema. Load the schema and execute POST /v1/ocr with the credential injected at run time. To run it on your own infrastructure, install Jentic One from its GitHub repo.

Is there an Auto-Reader MCP server?

You don't need an MCP server to give your agent the Auto-Reader API. Jentic connects it directly from the API Directory: import it, store your key once, and your agent calls the OCR and translation operations it needs. Operations are discovered on demand, so no extra server's tool definitions sit in the agent's context.

Can I limit what my agent is allowed to do with the Auto-Reader API?

Yes. Auto-Reader carries the image or text inside each request, so a rule bounds which operations your agent may call. Allow only the OCR and translation operations the task needs, so the data-deletion operation stays off the table, and every call runs through your own instance where it is logged for review.

Auto-Reader OCR + Translate API

AI ReadinessAI-Aware (B-)63/100

See full scorecard

Official vendor OpenAPI document · agent-readyAI/MLVisionbearer31 EndpointsREST

For Agents

Extract and translate text from images and documents: run multilingual OCR, detect and extract text regions, translate, and combine OCR with translation. Submit long documents and books as async jobs. Authenticate with a bearer key.

Use for: Extract the text from a scanned image, Translate a manga page from Japanese to English, Run OCR and translation on a document in one call, Submit a long book for asynchronous OCR

Not supported: Does not generate images, synthesize speech, or store documents long term. Use for OCR, text extraction, and translation only.

The Auto-Reader OCR + Translate API reads text from images and documents and translates it, with an Arabic-first and manga-aware recognition model. It detects and extracts text regions, runs OCR on images or base64 payloads, and can combine OCR and translation in a single call. Longer inputs run as asynchronous document and book jobs that a caller submits, polls, downloads, or deletes, and glossary, correction, and rewrite endpoints refine the extracted text.

Jentic One on Github View OpenAPI Document

Show dimension breakdown

Foundational Compliance

Base layer of spec validity and structural soundness.

Grade: A+Signals: 4

98%

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).

75%

Structural Integrity

Structural correctness score based on schema issues using logarithmic dampening.

Developer Experience & Jentic Compatibility

Clarity, completeness, and ingestion readiness for developers and tooling.

Grade: B-Signals: 4

Example Density

How richly the API is illustrated with examples.

100%

Example Validity

Percentage of examples that conform to their schemas.

49%

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.

AI-Readiness & Agent Experience

Semantic breadth, depth, and agent comprehension for AI systems.

Grade: DSignals: 4

22%

Description Coverage

Coverage of descriptions across API elements.

Error Standardization

Coverage of RFC 9457 Problem Details for error responses.

100%

OperationId Quality

Coverage, uniqueness, and casing consistency of operationIds for AI inference.

53%

Summary Coverage

Coverage of summaries across operations/tags/info.

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.

Security

Trust, risk posture, and security compliance.

Grade: B-Signals: 1

60%

Authentication Strength

Average quality of security schemes based on authentication method strength (weakest link for OAuth2).

AI Discoverability

Findability, semantic richness, and reasoning readiness.

Grade: C+Signals: 1

58%

Descriptive Richness

Clarity and depth of descriptions across API elements.

Connect the Auto-Reader OCR + Translate API to your agent

Capabilities

Start building with Auto-Reader OCR + Translate API

Use Cases

Key Endpoints

Why Jentic?

Related APIs

FAQs

Auto-Reader OCR + Translate API

Jentic AI Readiness Score

Auto-Reader OCR + Translate API

Foundational Compliance

Lint Results

Resolution Completeness

Specification Validity

Structural Integrity

Developer Experience & Jentic Compatibility

Example Density

Example Validity

Response Coverage

Tooling Readiness

AI-Readiness & Agent Experience

Description Coverage

Error Standardization

OperationId Quality

Summary Coverage

Agent Usability

Complexity Comfort

Security

Authentication Strength

AI Discoverability

Descriptive Richness