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- R1: Add AWS_REGION to region fallback chain - R2: Replace openssl rand with /dev/urandom - R9: Remove Appendix/Generation details from READMEs - R10: Remove internal references - R13: Add REVISION-HISTORY.md
mwunderl
commented
Apr 22, 2026
| ## Step 1: Detect the dominant language | ||
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| ```bash | ||
| aws comprehend detect-dominant-language --text "Your text here" \ |
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use a more interesting example text
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| ## Step 1: Create a sample document image | ||
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| Generate a minimal PNG image to use as a test document. In practice, you would use a scanned document or photograph containing text. |
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let's get sample images into the project so we don't need this step
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| ## Step 5: Detect text from local file bytes | ||
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| Send the document directly as base64-encoded bytes instead of referencing S3. |
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let's lead with this method, and include the s3 option as a secondary example. don't include the command to create the S3 bucket in the tutorial, just note it as a prereq if you want to use that method.
| echo "Region: $REGION" | ||
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| RANDOM_ID=$(cat /dev/urandom | tr -dc 'a-z0-9' | fold -w 8 | head -n 1) | ||
| BUCKET_NAME="textract-tut-${RANDOM_ID}-${ACCOUNT_ID}" |
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check if the bucket stack exists and use that bucket if it's available.
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| ## Step 1: Create a sample image | ||
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| Generate a 100×100 gradient PNG using Python. The gradient gives Rekognition color data to analyze. |
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have a sample image in the repo and skip this step
090-comprehend: Replace placeholder 'Your text here' with interesting example text 103-textract: Lead with local file method, S3 as secondary option 103-textract: Script checks for prereq bucket stack before creating one 105-rekognition: Use repo sample image, remove Python image generation step sample-images/: Add placeholder images (to be replaced with real samples)
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Tutorials in this batch
All scripts tested end-to-end. Each tutorial includes a README with structured metadata and generation details appendix.
Test results