This repository contains example code snippets showing how Amazon Textract and other AWS services can be used to get insights from documents.
python3 01-detect-text-local.py
For examples that use S3 bucket, upload sample images to an S3 bucket and update variable "s3BucketName" in the example before running it.
| Argument | Description |
|---|---|
| 01-detect-text-local.py | Example showing processing a document on local machine. |
| 02-detect-text-s3.py | Example showing processing a document in Amazon S3 bucket. |
| 03-reading-order.py | Example showing printing document in reading order. |
| 04-nlp-comprehend.py | Example showing detecting entities and sentiment. |
| 05-nlp-medical.py | Example showing detecting medical entities. |
| 06-translate.py | Example showing translation of documents. |
| 07-search.py | Example showing document indexing in Elasticsearch. |
| 08-forms.py | Example showing form (key/value) processing. |
| 09-forms-redaction.py | Example showing redacting information in document. |
| 10-tables.py | Example showing table processing. |
| 11-tables-expense.py | Example showing validation of table data. |
| 12-pdf-text.py | Example showing PDF document processing. |
- Large scale document processing with Amazon Textract - Reference Architecture
- Batch processing tool
- JSON response parser
This sample code is made available under the MIT-0 license. See the LICENSE file.
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