amazon-textract-textractor 1.0.0

A package to support the use of AWS Textract services.

Textractor is a python package created to seamlessly work with Amazon Textract a document intelligence service offering text recognition, table extraction, form processing, and much more. Whether you are making a one-off script or a complex distributed document processing pipeline, Textractor makes it easy to use Textract.

Installation

Textractor is available on PyPI and can be installed with pip install amazon-textract-textractor. By default this will install the minimal version of textractor. The following extras can be used to add features:

• pdf (pip install amazon-textract-textractor[pdf]) includes pdf2image and enables PDF rasterization in Textractor. Note that this is not necessary to call Textract with a PDF file.
• torch (pip install amazon-textract-textractor[torch]) includes sentence_transformers for better word search and matching. This will work on CPU but be noticeably slower than non-machine learning based approaches.
• dev (pip install amazon-textract-textractor[dev]) includes all the dependencies above and everything else needed to test the code.

You can pick several extras by separating the labels with commas like this pip install amazon-textract-textractor[pdf,torch].

Documentation

Generated documentation for the latest released version can be accessed here: aws-samples.github.io/amazon-textract-textractor/

Examples

Setup

These two lines are all you need to use Textract. The Textractor instance can be reused across multiple requests for both synchronous and asynchronous requests.

from textractor import Textractor

extractor = Textractor(aws_profile_name="default")

Text recognition

# file_source can be an image, list of images, bytes or S3 path
document = extractor.detect_document_text(file_source="tests/fixtures/single-page-1.png")
print(document.lines)
#[Textractor Test, Document, Page (1), Key - Values, Name of package: Textractor, Date : 08/14/2022, Table 1, Cell 1, Cell 2, Cell 4, Cell 5, Cell 6, Cell 7, Cell 8, Cell 9, Cell 10, Cell 11, Cell 12, Cell 13, Cell 14, Cell 15, Selection Element, Selected Checkbox, Un-Selected Checkbox]

Table extraction

from textractor.data.constants import TextractFeatures

document = extractor.analyze_document(
	file_source="tests/fixtures/acord_form_2_05272022.png",
	features=[TextractFeatures.TABLES]
)
# Saves the table in an excel document for further processing
document.tables[0].export_as_excel("output.xlsx")

Analyze ID

document = extractor.analyze_id(file_source="tests/fixtures/fake_id.jpg")
print(document.identity_documents[0].get("FIRST_NAME"))
# 'FAKEID'

Receipt processing (Analyze Expense)

document = extractor.analyze_expense(file_source="tests/fixtures/receipt.jpg")
print(document.expense_documents[0].get("TOTAL").text)
# '$1810.46'

If your use case was not covered here or if you are looking for asynchronous usage examples, see our collection of examples.

Try it out

The Demo.ipynb can be used as a reference to understand some functionalities hosted by the package. Additionally, docs/tests/notebooks/ have some tutorials you can try out.

Tests

The package comes with tests that call the production Textract APIs. Running the tests will incur charges to your AWS account.

Contributing

See CONTRIBUTING.md

License

This library is licensed under the Apache 2.0 License.