---
title: "AmazonTextractConverter"
id: amazontextractconverter
slug: "/amazontextractconverter"
description: "`AmazonTextractConverter` converts images and single-page PDFs to documents using AWS Textract. It supports plain text OCR, structured analysis of tables, forms, signatures, and layout, as well as natural-language queries over the document."
---

# AmazonTextractConverter

`AmazonTextractConverter` converts images and single-page PDFs to documents using AWS Textract. It supports plain text OCR, structured analysis of tables, forms, signatures, and layout, as well as natural-language queries over the document.

<div className="key-value-table">

|  |  |
| --- | --- |
| **Most common position in a pipeline** | Before [PreProcessors](../preprocessors.mdx), or right at the beginning of an indexing pipeline |
| **Mandatory init variables** | AWS credentials are resolved via `Secret` parameters or the default boto3 credential chain (environment variables, AWS config files, IAM roles). |
| **Mandatory run variables** | `sources`: A list of file paths or `ByteStream` objects |
| **Output variables** | `documents`: A list of documents <br /> <br />`raw_textract_response`: A list of raw responses from the Textract API |
| **API reference** | [Amazon Textract](/reference/integrations-amazon_textract) |
| **GitHub link** | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/amazon_textract |
| **Package name** | `amazon-textract-haystack` |

</div>

## Overview

`AmazonTextractConverter` takes a list of file paths or [`ByteStream`](../../concepts/data-classes.mdx#bytestream) objects as input and uses AWS Textract to extract text from images and single-page PDFs. Optionally, metadata can be attached to the documents through the `meta` input parameter. You need an active AWS account with access to the Textract service to use this integration. Refer to the [AWS Textract documentation](https://docs.aws.amazon.com/textract/latest/dg/getting-started.html) to set up your AWS credentials and ensure Textract is available in your selected region.

Supported input formats: JPEG, PNG, TIFF, BMP, and single-page PDF (up to 10 MB).

By default, the component uses the standard AWS environment variables (`AWS_ACCESS_KEY_ID`, `AWS_SECRET_ACCESS_KEY`, `AWS_SESSION_TOKEN`, `AWS_DEFAULT_REGION`, `AWS_PROFILE`) for authentication. You can also pass these as `Secret` objects at initialization. The component falls back to the default boto3 credential chain if no explicit credentials are provided, which makes it work with IAM roles when running on AWS infrastructure.

### Operation modes

The component switches between two Textract APIs depending on how you configure it:

- **Plain text OCR (`DetectDocumentText`)** – Used when `feature_types` is not set. This is the fastest and cheapest option, extracting raw text from the document.
- **Structured analysis (`AnalyzeDocument`)** – Used when `feature_types` is set. You can pass any combination of `"TABLES"`, `"FORMS"`, `"SIGNATURES"`, and `"LAYOUT"` to extract richer structural information from the document.

### Natural-language queries

You can pass a list of natural-language questions through the `queries` parameter on `run()`. When queries are provided, the `QUERIES` feature type is added automatically and Textract returns the extracted answers in the raw response. This is useful for pulling specific fields out of forms, invoices, or receipts without writing custom parsing logic.

## Usage

You need to install the `amazon-textract-haystack` integration to use `AmazonTextractConverter`:

```shell
pip install amazon-textract-haystack
```

### On its own

Basic usage with plain text OCR:

```python
from haystack_integrations.components.converters.amazon_textract import (
    AmazonTextractConverter,
)

converter = AmazonTextractConverter()
result = converter.run(sources=["document.png"])
documents = result["documents"]
```

Extracting tables and forms with `AnalyzeDocument`:

```python
from haystack_integrations.components.converters.amazon_textract import (
    AmazonTextractConverter,
)

converter = AmazonTextractConverter(feature_types=["TABLES", "FORMS"])
converter.warm_up()
result = converter.run(sources=["invoice.pdf"])
documents = result["documents"]
raw_responses = result["raw_textract_response"]
```

Using natural-language queries to extract specific fields:

```python
from haystack_integrations.components.converters.amazon_textract import (
    AmazonTextractConverter,
)

converter = AmazonTextractConverter()
result = converter.run(
    sources=["receipt.png"],
    queries=["What is the patient name?", "What is the total due?"],
)
documents = result["documents"]
raw_responses = result["raw_textract_response"]
```

Passing AWS credentials explicitly:

```python
from haystack.utils import Secret
from haystack_integrations.components.converters.amazon_textract import (
    AmazonTextractConverter,
)

converter = AmazonTextractConverter(
    aws_access_key_id=Secret.from_env_var("AWS_ACCESS_KEY_ID"),
    aws_secret_access_key=Secret.from_env_var("AWS_SECRET_ACCESS_KEY"),
    aws_region_name=Secret.from_token("us-east-1"),
)
result = converter.run(sources=["document.png"])
```

### In a pipeline

Here's an example of an indexing pipeline that uses Textract to extract text from images and writes the resulting documents to a Document Store:

```python
from haystack import Pipeline
from haystack.document_stores.in_memory import InMemoryDocumentStore
from haystack.components.preprocessors import DocumentCleaner, DocumentSplitter
from haystack.components.writers import DocumentWriter
from haystack_integrations.components.converters.amazon_textract import (
    AmazonTextractConverter,
)

document_store = InMemoryDocumentStore()

pipeline = Pipeline()
pipeline.add_component("converter", AmazonTextractConverter())
pipeline.add_component("cleaner", DocumentCleaner())
pipeline.add_component(
    "splitter",
    DocumentSplitter(split_by="sentence", split_length=5),
)
pipeline.add_component("writer", DocumentWriter(document_store=document_store))

pipeline.connect("converter", "cleaner")
pipeline.connect("cleaner", "splitter")
pipeline.connect("splitter", "writer")

file_names = ["document.png", "invoice.pdf"]
pipeline.run({"converter": {"sources": file_names}})
```
