There are more AWS SDK examples available in the [AWS Doc SDK Examples](https://github.com/awsdocs/aws-doc-sdk-examples) GitHub repo. # Scenarios for Amazon Textract using AWS SDKs The following code examples show you how to implement common scenarios in Amazon Textract with AWS SDKs. These scenarios show you how to accomplish specific tasks by calling multiple functions within Amazon Textract or combined with other AWS services. Each scenario includes a link to the complete source code, where you can find instructions on how to set up and run the code. Scenarios target an intermediate level of experience to help you understand service actions in context. **Topics** + [Create an Amazon Textract explorer application](textract_example_cross_TextractExplorer_section.md) + [Create an application to analyze customer feedback](textract_example_cross_FSA_section.md) + [Detect entities in text extracted from an image](textract_example_cross_TextractComprehendDetectEntities_section.md) + [Get started with document analysis](textract_example_textract_Scenario_GettingStarted_section.md) + [Getting started with Amazon Textract](textract_example_s3_GettingStarted_074_section.md) # Create an Amazon Textract explorer application The following code examples show how to explore Amazon Textract output through an interactive application. ------ #### [ JavaScript ] **SDK for JavaScript (v3)** Shows how to use the AWS SDK for JavaScript to build a React application that uses Amazon Textract to extract data from a document image and display it in an interactive web page. This example runs in a web browser and requires an authenticated Amazon Cognito identity for credentials. It uses Amazon Simple Storage Service (Amazon S3) for storage, and for notifications it polls an Amazon Simple Queue Service (Amazon SQS) queue that is subscribed to an Amazon Simple Notification Service (Amazon SNS) topic. For complete source code and instructions on how to set up and run, see the full example on [GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javascriptv3/example_code/cross-services/textract-react). **Services used in this example** + Amazon Cognito Identity + Amazon S3 + Amazon SNS + Amazon SQS + Amazon Textract ------ #### [ Python ] **SDK for Python (Boto3)** Shows how to use the AWS SDK for Python (Boto3) with Amazon Textract to detect text, form, and table elements in a document image. The input image and Amazon Textract output are shown in a Tkinter application that lets you explore the detected elements. + Submit a document image to Amazon Textract and explore the output of detected elements. + Submit images directly to Amazon Textract or through an Amazon Simple Storage Service (Amazon S3) bucket. + Use asynchronous APIs to start a job that publishes a notification to an Amazon Simple Notification Service (Amazon SNS) topic when the job completes. + Poll an Amazon Simple Queue Service (Amazon SQS) queue for a job completion message and display the results. For complete source code and instructions on how to set up and run, see the full example on [GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/cross_service/textract_explorer). **Services used in this example** + Amazon Cognito Identity + Amazon S3 + Amazon SNS + Amazon SQS + Amazon Textract ------ # Create an application that analyzes customer feedback and synthesizes audio The following code examples show how to create an application that analyzes customer comment cards, translates them from their original language, determines their sentiment, and generates an audio file from the translated text. ------ #### [ .NET ] **SDK for .NET** This example application analyzes and stores customer feedback cards. Specifically, it fulfills the need of a fictitious hotel in New York City. The hotel receives feedback from guests in various languages in the form of physical comment cards. That feedback is uploaded into the app through a web client. After an image of a comment card is uploaded, the following steps occur: + Text is extracted from the image using Amazon Textract. + Amazon Comprehend determines the sentiment of the extracted text and its language. + The extracted text is translated to English using Amazon Translate. + Amazon Polly synthesizes an audio file from the extracted text. The full app can be deployed with the AWS CDK. For source code and deployment instructions, see the project in [ GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/dotnetv3/cross-service/FeedbackSentimentAnalyzer). **Services used in this example** + Amazon Comprehend + Lambda + Amazon Polly + Amazon Textract + Amazon Translate ------ #### [ Java ] **SDK for Java 2.x** This example application analyzes and stores customer feedback cards. Specifically, it fulfills the need of a fictitious hotel in New York City. The hotel receives feedback from guests in various languages in the form of physical comment cards. That feedback is uploaded into the app through a web client. After an image of a comment card is uploaded, the following steps occur: + Text is extracted from the image using Amazon Textract. + Amazon Comprehend determines the sentiment of the extracted text and its language. + The extracted text is translated to English using Amazon Translate. + Amazon Polly synthesizes an audio file from the extracted text. The full app can be deployed with the AWS CDK. For source code and deployment instructions, see the project in [ GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javav2/usecases/creating_fsa_app). **Services used in this example** + Amazon Comprehend + Lambda + Amazon Polly + Amazon Textract + Amazon Translate ------ #### [ JavaScript ] **SDK for JavaScript (v3)** This example application analyzes and stores customer feedback cards. Specifically, it fulfills the need of a fictitious hotel in New York City. The hotel receives feedback from guests in various languages in the form of physical comment cards. That feedback is uploaded into the app through a web client. After an image of a comment card is uploaded, the following steps occur: + Text is extracted from the image using Amazon Textract. + Amazon Comprehend determines the sentiment of the extracted text and its language. + The extracted text is translated to English using Amazon Translate. + Amazon Polly synthesizes an audio file from the extracted text. The full app can be deployed with the AWS CDK. For source code and deployment instructions, see the project in [ GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javascriptv3/example_code/cross-services/feedback-sentiment-analyzer). The following excerpts show how the AWS SDK for JavaScript is used inside of Lambda functions. ``` import { ComprehendClient, DetectDominantLanguageCommand, DetectSentimentCommand, } from "@aws-sdk/client-comprehend"; /** * Determine the language and sentiment of the extracted text. * * @param {{ source_text: string}} extractTextOutput */ export const handler = async (extractTextOutput) => { const comprehendClient = new ComprehendClient({}); const detectDominantLanguageCommand = new DetectDominantLanguageCommand({ Text: extractTextOutput.source_text, }); // The source language is required for sentiment analysis and // translation in the next step. const { Languages } = await comprehendClient.send( detectDominantLanguageCommand, ); const languageCode = Languages[0].LanguageCode; const detectSentimentCommand = new DetectSentimentCommand({ Text: extractTextOutput.source_text, LanguageCode: languageCode, }); const { Sentiment } = await comprehendClient.send(detectSentimentCommand); return { sentiment: Sentiment, language_code: languageCode, }; }; ``` ``` import { DetectDocumentTextCommand, TextractClient, } from "@aws-sdk/client-textract"; /** * Fetch the S3 object from the event and analyze it using Amazon Textract. * * @param {import("@types/aws-lambda").EventBridgeEvent<"Object Created">} eventBridgeS3Event */ export const handler = async (eventBridgeS3Event) => { const textractClient = new TextractClient(); const detectDocumentTextCommand = new DetectDocumentTextCommand({ Document: { S3Object: { Bucket: eventBridgeS3Event.bucket, Name: eventBridgeS3Event.object, }, }, }); // Textract returns a list of blocks. A block can be a line, a page, word, etc. // Each block also contains geometry of the detected text. // For more information on the Block type, see https://docs.aws.amazon.com/textract/latest/dg/API_Block.html. const { Blocks } = await textractClient.send(detectDocumentTextCommand); // For the purpose of this example, we are only interested in words. const extractedWords = Blocks.filter((b) => b.BlockType === "WORD").map( (b) => b.Text, ); return extractedWords.join(" "); }; ``` ``` import { PollyClient, SynthesizeSpeechCommand } from "@aws-sdk/client-polly"; import { S3Client } from "@aws-sdk/client-s3"; import { Upload } from "@aws-sdk/lib-storage"; /** * Synthesize an audio file from text. * * @param {{ bucket: string, translated_text: string, object: string}} sourceDestinationConfig */ export const handler = async (sourceDestinationConfig) => { const pollyClient = new PollyClient({}); const synthesizeSpeechCommand = new SynthesizeSpeechCommand({ Engine: "neural", Text: sourceDestinationConfig.translated_text, VoiceId: "Ruth", OutputFormat: "mp3", }); const { AudioStream } = await pollyClient.send(synthesizeSpeechCommand); const audioKey = `${sourceDestinationConfig.object}.mp3`; // Store the audio file in S3. const s3Client = new S3Client(); const upload = new Upload({ client: s3Client, params: { Bucket: sourceDestinationConfig.bucket, Key: audioKey, Body: AudioStream, ContentType: "audio/mp3", }, }); await upload.done(); return audioKey; }; ``` ``` import { TranslateClient, TranslateTextCommand, } from "@aws-sdk/client-translate"; /** * Translate the extracted text to English. * * @param {{ extracted_text: string, source_language_code: string}} textAndSourceLanguage */ export const handler = async (textAndSourceLanguage) => { const translateClient = new TranslateClient({}); const translateCommand = new TranslateTextCommand({ SourceLanguageCode: textAndSourceLanguage.source_language_code, TargetLanguageCode: "en", Text: textAndSourceLanguage.extracted_text, }); const { TranslatedText } = await translateClient.send(translateCommand); return { translated_text: TranslatedText }; }; ``` **Services used in this example** + Amazon Comprehend + Lambda + Amazon Polly + Amazon Textract + Amazon Translate ------ #### [ Ruby ] **SDK for Ruby** This example application analyzes and stores customer feedback cards. Specifically, it fulfills the need of a fictitious hotel in New York City. The hotel receives feedback from guests in various languages in the form of physical comment cards. That feedback is uploaded into the app through a web client. After an image of a comment card is uploaded, the following steps occur: + Text is extracted from the image using Amazon Textract. + Amazon Comprehend determines the sentiment of the extracted text and its language. + The extracted text is translated to English using Amazon Translate. + Amazon Polly synthesizes an audio file from the extracted text. The full app can be deployed with the AWS CDK. For source code and deployment instructions, see the project in [ GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/ruby/cross_service_examples/feedback_sentiment_analyzer). **Services used in this example** + Amazon Comprehend + Lambda + Amazon Polly + Amazon Textract + Amazon Translate ------ # Detect entities in text extracted from an image using an AWS SDK The following code example shows how to use Amazon Comprehend to detect entities in text extracted by Amazon Textract from an image that is stored in Amazon S3. ------ #### [ Python ] **SDK for Python (Boto3)** Shows how to use the AWS SDK for Python (Boto3) in a Jupyter notebook to detect entities in text that is extracted from an image. This example uses Amazon Textract to extract text from an image stored in Amazon Simple Storage Service (Amazon S3) and Amazon Comprehend to detect entities in the extracted text. This example is a Jupyter notebook and must be run in an environment that can host notebooks. For instructions on how to run the example using Amazon SageMaker AI, see the directions in [TextractAndComprehendNotebook.ipynb](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/cross_service/textract_comprehend_notebook/TextractAndComprehendNotebook.ipynb). For complete source code and instructions on how to set up and run, see the full example on [GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/cross_service/textract_comprehend_notebook#readme). **Services used in this example** + Amazon Comprehend + Amazon S3 + Amazon Textract ------ # Get started with Amazon Textract document analysis using an AWS SDK The following code example shows how to: + Start asynchronous analysis. + Get document analysis. ------ #### [ SAP ABAP ] **SDK for SAP ABAP** There's more on GitHub. Find the complete example and learn how to set up and run in the [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/tex#code-examples). ``` "Create ABAP objects for feature type." "Add TABLES to return information about the tables." "Add FORMS to return detected form data." "To perform both types of analysis, add TABLES and FORMS to FeatureTypes." DATA(lt_featuretypes) = VALUE /aws1/cl_texfeaturetypes_w=>tt_featuretypes( ( NEW /aws1/cl_texfeaturetypes_w( iv_value = 'FORMS' ) ) ( NEW /aws1/cl_texfeaturetypes_w( iv_value = 'TABLES' ) ) ). "Create an ABAP object for the Amazon Simple Storage Service (Amazon S3) object." DATA(lo_s3object) = NEW /aws1/cl_texs3object( iv_bucket = iv_s3bucket iv_name = iv_s3object ). "Create an ABAP object for the document." DATA(lo_documentlocation) = NEW /aws1/cl_texdocumentlocation( io_s3object = lo_s3object ). "Start document analysis." TRY. DATA(lo_start_result) = lo_tex->startdocumentanalysis( io_documentlocation = lo_documentlocation it_featuretypes = lt_featuretypes ). MESSAGE 'Document analysis started.' TYPE 'I'. CATCH /aws1/cx_texaccessdeniedex. MESSAGE 'You do not have permission to perform this action.' TYPE 'E'. CATCH /aws1/cx_texbaddocumentex. MESSAGE 'Amazon Textract is not able to read the document.' TYPE 'E'. CATCH /aws1/cx_texdocumenttoolargeex. MESSAGE 'The document is too large.' TYPE 'E'. CATCH /aws1/cx_texidempotentprmmis00. MESSAGE 'Idempotent parameter mismatch exception.' TYPE 'E'. CATCH /aws1/cx_texinternalservererr. MESSAGE 'Internal server error.' TYPE 'E'. CATCH /aws1/cx_texinvalidkmskeyex. MESSAGE 'AWS KMS key is not valid.' TYPE 'E'. CATCH /aws1/cx_texinvalidparameterex. MESSAGE 'Request has non-valid parameters.' TYPE 'E'. CATCH /aws1/cx_texinvalids3objectex. MESSAGE 'Amazon S3 object is not valid.' TYPE 'E'. CATCH /aws1/cx_texlimitexceededex. MESSAGE 'An Amazon Textract service limit was exceeded.' TYPE 'E'. CATCH /aws1/cx_texprovthruputexcdex. MESSAGE 'Provisioned throughput exceeded limit.' TYPE 'E'. CATCH /aws1/cx_texthrottlingex. MESSAGE 'The request processing exceeded the limit.' TYPE 'E'. CATCH /aws1/cx_texunsupporteddocex. MESSAGE 'The document is not supported.' TYPE 'E'. ENDTRY. "Get job ID from the output." DATA(lv_jobid) = lo_start_result->get_jobid( ). "Wait for job to complete." oo_result = lo_tex->getdocumentanalysis( iv_jobid = lv_jobid ). " oo_result is returned for testing purposes. " WHILE oo_result->get_jobstatus( ) <> 'SUCCEEDED'. IF sy-index = 10. EXIT. "Maximum 300 seconds." ENDIF. WAIT UP TO 30 SECONDS. oo_result = lo_tex->getdocumentanalysis( iv_jobid = lv_jobid ). ENDWHILE. DATA(lt_blocks) = oo_result->get_blocks( ). LOOP AT lt_blocks INTO DATA(lo_block). IF lo_block->get_text( ) = 'INGREDIENTS: POWDERED SUGAR* (CANE SUGAR,'. MESSAGE 'Found text in the doc: ' && lo_block->get_text( ) TYPE 'I'. ENDIF. ENDLOOP. ``` + For API details, see the following topics in *AWS SDK for SAP ABAP API reference*. + [GetDocumentAnalysis](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html) + [StartDocumentAnalysis](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html) ------ # Getting started with Amazon Textract The following code example shows how to: + Create an S3 bucket + Upload a document to S3 + Clean up resources ------ #### [ Bash ] **AWS CLI with Bash script** There's more on GitHub. Find the complete example and learn how to set up and run in the [Sample developer tutorials](https://github.com/aws-samples/sample-developer-tutorials/tree/main/tuts/074-amazon-textract-gs) repository. ``` #!/bin/bash # Amazon Textract Getting Started Tutorial Script # This script demonstrates how to use Amazon Textract to analyze document text # Set up logging LOG_FILE="textract-tutorial.log" exec > >(tee -a "$LOG_FILE") 2>&1 echo "===================================================" echo "Amazon Textract Getting Started Tutorial" echo "===================================================" echo "This script will guide you through using Amazon Textract to analyze document text." echo "" # Function to check for errors in command output and exit code check_error() { local exit_code=$1 local output=$2 local cmd=$3 if [ $exit_code -ne 0 ] || echo "$output" | grep -i "error" > /dev/null; then echo "ERROR: Command failed: $cmd" echo "$output" cleanup_on_error exit 1 fi } # Function to clean up resources on error cleanup_on_error() { echo "Error encountered. Cleaning up resources..." # Clean up temporary JSON files if [ -f "document.json" ]; then rm -f document.json fi if [ -f "features.json" ]; then rm -f features.json fi if [ -n "$DOCUMENT_NAME" ] && [ -n "$BUCKET_NAME" ]; then echo "Deleting document from S3..." aws s3 rm "s3://$BUCKET_NAME/$DOCUMENT_NAME" || echo "Failed to delete document" fi if [ -n "$BUCKET_NAME" ]; then echo "Deleting S3 bucket..." aws s3 rb "s3://$BUCKET_NAME" --force || echo "Failed to delete bucket" fi } # Verify AWS CLI is installed and configured echo "Verifying AWS CLI configuration..." AWS_CONFIG_OUTPUT=$(aws configure list 2>&1) AWS_CONFIG_STATUS=$? if [ $AWS_CONFIG_STATUS -ne 0 ]; then echo "ERROR: AWS CLI is not properly configured." echo "$AWS_CONFIG_OUTPUT" exit 1 fi # Verify AWS region is configured and supports Textract AWS_REGION=$(aws configure get region) if [ -z "$AWS_REGION" ]; then echo "ERROR: No AWS region configured. Please run 'aws configure' to set a default region." exit 1 fi # Check if Textract is available in the configured region echo "Checking if Amazon Textract is available in region $AWS_REGION..." TEXTRACT_CHECK=$(aws textract help 2>&1) TEXTRACT_CHECK_STATUS=$? if [ $TEXTRACT_CHECK_STATUS -ne 0 ]; then echo "ERROR: Amazon Textract may not be available in region $AWS_REGION." echo "$TEXTRACT_CHECK" exit 1 fi # Generate a random identifier for S3 bucket RANDOM_ID=$(openssl rand -hex 6) BUCKET_NAME="textract-${RANDOM_ID}" DOCUMENT_NAME="document.png" RESOURCES_CREATED=() # Step 1: Create S3 bucket echo "Creating S3 bucket: $BUCKET_NAME" CREATE_BUCKET_OUTPUT=$(aws s3 mb "s3://$BUCKET_NAME" 2>&1) CREATE_BUCKET_STATUS=$? echo "$CREATE_BUCKET_OUTPUT" check_error $CREATE_BUCKET_STATUS "$CREATE_BUCKET_OUTPUT" "aws s3 mb s3://$BUCKET_NAME" RESOURCES_CREATED+=("S3 Bucket: $BUCKET_NAME") # Step 2: Check if sample document exists, if not create a simple one if [ ! -f "$DOCUMENT_NAME" ]; then echo "Sample document not found. Please provide a document to analyze." echo "Enter the path to your document (must be an image file like PNG or JPEG):" read -r DOCUMENT_PATH if [ ! -f "$DOCUMENT_PATH" ]; then echo "File not found: $DOCUMENT_PATH" cleanup_on_error exit 1 fi DOCUMENT_NAME=$(basename "$DOCUMENT_PATH") echo "Using document: $DOCUMENT_PATH as $DOCUMENT_NAME" # Copy the document to the current directory cp "$DOCUMENT_PATH" "./$DOCUMENT_NAME" fi # Step 3: Upload document to S3 echo "Uploading document to S3..." UPLOAD_OUTPUT=$(aws s3 cp "./$DOCUMENT_NAME" "s3://$BUCKET_NAME/" 2>&1) UPLOAD_STATUS=$? echo "$UPLOAD_OUTPUT" check_error $UPLOAD_STATUS "$UPLOAD_OUTPUT" "aws s3 cp ./$DOCUMENT_NAME s3://$BUCKET_NAME/" RESOURCES_CREATED+=("S3 Object: s3://$BUCKET_NAME/$DOCUMENT_NAME") # Step 4: Analyze document with Amazon Textract echo "Analyzing document with Amazon Textract..." echo "This may take a few seconds..." # Create a JSON file for the document parameter to avoid shell escaping issues cat > document.json << EOF { "S3Object": { "Bucket": "$BUCKET_NAME", "Name": "$DOCUMENT_NAME" } } EOF # Create a JSON file for the feature types parameter cat > features.json << EOF ["TABLES","FORMS","SIGNATURES"] EOF ANALYZE_OUTPUT=$(aws textract analyze-document --document file://document.json --feature-types file://features.json 2>&1) ANALYZE_STATUS=$? echo "Analysis complete." if [ $ANALYZE_STATUS -ne 0 ]; then echo "ERROR: Document analysis failed" echo "$ANALYZE_OUTPUT" cleanup_on_error exit 1 fi # Save the analysis results to a file echo "$ANALYZE_OUTPUT" > textract-analysis-results.json echo "Analysis results saved to textract-analysis-results.json" RESOURCES_CREATED+=("Local file: textract-analysis-results.json") # Display a summary of the analysis echo "" echo "===================================================" echo "Analysis Summary" echo "===================================================" PAGES=$(echo "$ANALYZE_OUTPUT" | grep -o '"Pages": [0-9]*' | awk '{print $2}') echo "Document pages: $PAGES" BLOCKS_COUNT=$(echo "$ANALYZE_OUTPUT" | grep -o '"BlockType":' | wc -l) echo "Total blocks detected: $BLOCKS_COUNT" # Count different block types PAGE_COUNT=$(echo "$ANALYZE_OUTPUT" | grep -o '"BlockType": "PAGE"' | wc -l) LINE_COUNT=$(echo "$ANALYZE_OUTPUT" | grep -o '"BlockType": "LINE"' | wc -l) WORD_COUNT=$(echo "$ANALYZE_OUTPUT" | grep -o '"BlockType": "WORD"' | wc -l) TABLE_COUNT=$(echo "$ANALYZE_OUTPUT" | grep -o '"BlockType": "TABLE"' | wc -l) CELL_COUNT=$(echo "$ANALYZE_OUTPUT" | grep -o '"BlockType": "CELL"' | wc -l) KEY_VALUE_COUNT=$(echo "$ANALYZE_OUTPUT" | grep -o '"BlockType": "KEY_VALUE_SET"' | wc -l) SIGNATURE_COUNT=$(echo "$ANALYZE_OUTPUT" | grep -o '"BlockType": "SIGNATURE"' | wc -l) echo "Pages: $PAGE_COUNT" echo "Lines of text: $LINE_COUNT" echo "Words: $WORD_COUNT" echo "Tables: $TABLE_COUNT" echo "Table cells: $CELL_COUNT" echo "Key-value pairs: $KEY_VALUE_COUNT" echo "Signatures: $SIGNATURE_COUNT" echo "" # Cleanup confirmation echo "" echo "===================================================" echo "RESOURCES CREATED" echo "===================================================" for resource in "${RESOURCES_CREATED[@]}"; do echo "- $resource" done echo "" echo "===================================================" echo "CLEANUP CONFIRMATION" echo "===================================================" echo "Do you want to clean up all created resources? (y/n): " read -r CLEANUP_CHOICE if [[ "$CLEANUP_CHOICE" =~ ^[Yy] ]]; then echo "Cleaning up resources..." # Delete document from S3 echo "Deleting document from S3..." DELETE_DOC_OUTPUT=$(aws s3 rm "s3://$BUCKET_NAME/$DOCUMENT_NAME" 2>&1) DELETE_DOC_STATUS=$? echo "$DELETE_DOC_OUTPUT" check_error $DELETE_DOC_STATUS "$DELETE_DOC_OUTPUT" "aws s3 rm s3://$BUCKET_NAME/$DOCUMENT_NAME" # Delete S3 bucket echo "Deleting S3 bucket..." DELETE_BUCKET_OUTPUT=$(aws s3 rb "s3://$BUCKET_NAME" --force 2>&1) DELETE_BUCKET_STATUS=$? echo "$DELETE_BUCKET_OUTPUT" check_error $DELETE_BUCKET_STATUS "$DELETE_BUCKET_OUTPUT" "aws s3 rb s3://$BUCKET_NAME --force" # Delete local JSON files rm -f document.json features.json echo "Cleanup complete. The analysis results file (textract-analysis-results.json) has been kept." else echo "Resources have been preserved." fi echo "" echo "===================================================" echo "Tutorial complete!" echo "===================================================" echo "You have successfully analyzed a document using Amazon Textract." echo "The analysis results are available in textract-analysis-results.json" echo "" ``` + For API details, see the following topics in *AWS CLI Command Reference*. + [AnalyzeDocument](https://docs.aws.amazon.com/goto/aws-cli/textract-2018-06-27/AnalyzeDocument) + [Cp](https://docs.aws.amazon.com/goto/aws-cli/s3-2006-03-01/Cp) + [Help](https://docs.aws.amazon.com/goto/aws-cli/textract-2018-06-27/Help) + [Mb](https://docs.aws.amazon.com/goto/aws-cli/s3-2006-03-01/Mb) + [Rb](https://docs.aws.amazon.com/goto/aws-cli/s3-2006-03-01/Rb) + [Rm](https://docs.aws.amazon.com/goto/aws-cli/s3-2006-03-01/Rm) ------