Amazon Textract vs Comprehend for Document Processing
Comparing Amazon Textract and Amazon Comprehend for document processing workflows, covering text extraction, entity recognition, and when to use each.
Textract and Comprehend are both AWS AI services used in document processing, but they solve different problems. Textract extracts text and structure from documents. Comprehend analyzes text to extract meaning. Most document processing pipelines need both, used sequentially.
Overview
| Aspect | Amazon Textract | Amazon Comprehend |
|---|---|---|
| Primary Function | Text and structure extraction from images/PDFs | NLP analysis of text |
| Input | Images, PDFs, scanned documents | Plain text |
| Output | Text, tables, forms, layout | Entities, sentiment, key phrases, topics |
| OCR | Built-in | Not included |
| Custom Models | Custom queries, adapters | Custom entity recognition, classification |
| Pricing | Per-page | Per-unit (100 characters), 3-unit minimum per request |
What Textract Does
Textract is an OCR and document understanding service. It extracts text from scanned documents, photos of documents, and PDFs. Beyond raw text extraction, Textract understands document structure:
- DetectDocumentText extracts raw text (printed and handwritten) with word and line-level bounding boxes
- AnalyzeDocument extracts forms (key-value pairs), tables with cell-level structure, layout elements (paragraphs, titles, lists, headers, footers), and detected signatures
- AnalyzeExpense specializes in invoices and receipts with pre-trained field extraction
- AnalyzeID extracts structured data from identity documents
- AnalyzeLending is a managed workflow that classifies, splits, and extracts data from mortgage and loan packages
Textract’s Queries feature lets you ask natural language questions about a document (“What is the patient name?”) and get targeted extraction results. Custom Queries lets you adapt the pretrained Queries feature to business-specific document types by annotating as few as ten sample documents and training an adapter (up to 30 queries per adapter).
In June 2025, AWS updated the text detection models behind DetectDocumentText and AnalyzeDocument to add support for superscripts, subscripts, and rotated text, along with accuracy gains on box forms, visually similar characters (such as ‘0’ versus ‘O’), and lower-resolution documents like faxes.
What Comprehend Does
Comprehend is a natural language processing (NLP) service. It takes text as input (not images) and extracts meaning:
- Entity Recognition identifies people, organizations, dates, quantities, and other entities
- Sentiment Analysis classifies text as positive, negative, neutral, or mixed (targeted sentiment ties sentiment to specific entities)
- Key Phrase Extraction identifies significant phrases
- Language Detection identifies the document language
- PII Detection identifies and redacts personally identifiable information
- Toxicity Detection flags harmful content for moderation and for screening generative AI output
Comprehend also supports custom models. Custom Entity Recognition lets you train models to extract domain-specific entities. Custom Classification trains document classifiers on your labeled data. For both, Comprehend can perform text extraction from image, PDF, or Word inputs automatically before analysis, so you do not always need a separate OCR step.
Note a 2026 change: Comprehend topic modeling, event detection, and prompt safety classification are no longer available to new customers, effective April 30, 2026 (announced March 31, 2026). Accounts that used these features within the prior 12 months keep access. AWS now points new workloads to Amazon Bedrock for topic and event extraction, and to Amazon Bedrock Guardrails for prompt safety. The core capabilities above remain unaffected.
How They Work Together
The standard document processing pipeline uses Textract first, then Comprehend:
- Textract extracts text and structure from scanned documents
- Comprehend analyzes the extracted text for entities, classification, or PII detection
For example, processing insurance claims: Textract extracts text from scanned claim forms, identifies form fields and table data. Comprehend then classifies the claim type, extracts named entities (claimant, dates, amounts), and detects PII for redaction.
When to Use Textract Alone
Use Textract alone when your goal is structured data extraction from documents and you do not need NLP analysis. Digitizing paper forms, extracting tables from PDFs, processing invoices, and reading identity documents are all Textract-only workflows. Textract’s Queries feature and specialized analyzers (Expense, ID, Lending) handle many extraction tasks without needing Comprehend.
When to Use Comprehend Alone
Use Comprehend alone when your text is already digital and you need NLP analysis. Analyzing customer reviews, classifying support tickets, detecting PII in text databases, and topic modeling document collections are Comprehend-only workflows. If your input is already text (not scanned documents or images), Textract is unnecessary.
When to Use Both
Use both when you have scanned or image-based documents and need to understand their content beyond structure. Medical record processing, legal document analysis, and compliance document review typically require Textract for extraction and Comprehend for entity recognition, classification, or PII detection.
Practical Recommendation
Think of Textract as the “eyes” (reading documents) and Comprehend as the “brain” (understanding text). If your documents are digital text, skip Textract. If you only need to extract structured fields, Textract alone may suffice. For end-to-end document intelligence that extracts, classifies, and understands document content, combine both services in a Step Functions workflow with S3 for document storage.