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Amazon Textract
Extracts printed text, handwriting, tables, forms, and document structure
Why learn Amazon Textract?
Teams use Amazon Textract to adopt intelligent document processing without building the complete underlying platform themselves.
The problem it solves
Extracts structured text and fields from forms and documents
Where it came from
Amazon Textract is Amazon Web Services's offering for intelligent document processing. Its closest catalog counterparts are Azure AI Document Intelligence and Google Cloud Document AI; they address the same broad need but are not feature-for-feature identical.
Build your understanding
Work through these exercises in order. Try the task before opening a reference.
- Explain the main responsibility of Amazon Textract.
- Choose a workload that fits Amazon Textract.
- Identify one operational tradeoff of Amazon Textract.
- Compare Amazon Textract with Azure AI Document Intelligence.
- Compare Amazon Textract with Google Cloud Document AI.
Documentation and sources
- AWS cloud services catalog (aws.amazon.com)
Connect the ideas
Alternatives
Real World Example
Automated Logistics Manifest Digitization
A regional freight logistics company managing thousands of handwritten and printed shipping manifests daily.
The business problem
Manual data entry from paper-based shipping manifests is prone to human error, creates significant processing delays, and prevents real-time inventory tracking.
How this topic solves it
Amazon Textract automates the extraction of printed text, handwriting, tables, and forms from scanned manifests, converting unstructured paper documents into machine-readable JSON data for downstream logistics systems.
- Upload scanned manifest images to an Amazon S3 bucket.
- Trigger an AWS Lambda function upon S3 object creation.
- Call the AnalyzeDocument API to extract tables and key-value pairs.
- Parse the returned JSON response to map fields like 'Tracking Number' and 'Weight' to the database.
- Update the logistics management system with the extracted data.
This diagram illustrates the flow from physical document to database update.
Storage for scans
Extraction engine
Final data store
- Physical Manifest → Upload scan → S3 Bucket
- S3 Bucket → Trigger analysis → Textract
- Textract → Write JSON data → Logistics DB
Expected business outcome
The company achieves rapid, accurate data ingestion from physical documents, enabling immediate visibility into shipment status without manual intervention.
Limitations and checks
Textract may struggle with low-resolution scans or highly distorted handwriting, which can lead to extraction errors; developers should implement a confidence score threshold check to flag low-confidence results for human review.