There are more AWS SDK examples available in the [AWS Doc SDK Examples](https://github.com/awsdocs/aws-doc-sdk-examples) GitHub repo. # Code examples for Amazon Textract using AWS SDKs The following code examples show you how to use Amazon Textract with an AWS software development kit (SDK). *Actions* are code excerpts from larger programs and must be run in context. While actions show you how to call individual service functions, you can see actions in context in their related scenarios. *Scenarios* are code examples that show you how to accomplish specific tasks by calling multiple functions within a service or combined with other AWS services. **More resources** + **[ Amazon Textract Developer Guide](https://docs.aws.amazon.com/textract/latest/dg/what-is.html)** – More information about Amazon Textract. + **[Amazon Textract API Reference](https://docs.aws.amazon.com/textract/latest/dg/API_Reference.html)** – Details about all available Amazon Textract actions. + **[AWS Developer Center](https://aws.amazon.com/developer/code-examples/?awsf.sdk-code-examples-product=product%23textract)** – Code examples that you can filter by category or full-text search. + **[AWS SDK Examples](https://github.com/awsdocs/aws-doc-sdk-examples)** – GitHub repo with complete code in preferred languages. Includes instructions for setting up and running the code. **Contents** + [Basics](textract_code_examples_basics.md) + [Actions](textract_code_examples_actions.md) + [`AnalyzeDocument`](textract_example_textract_AnalyzeDocument_section.md) + [`DetectDocumentText`](textract_example_textract_DetectDocumentText_section.md) + [`GetDocumentAnalysis`](textract_example_textract_GetDocumentAnalysis_section.md) + [`StartDocumentAnalysis`](textract_example_textract_StartDocumentAnalysis_section.md) + [`StartDocumentTextDetection`](textract_example_textract_StartDocumentTextDetection_section.md) + [Scenarios](textract_code_examples_scenarios.md) + [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) # Basic examples for Amazon Textract using AWS SDKs The following code examples show how to use the basics of Amazon Textract with AWS SDKs. **Contents** + [Actions](textract_code_examples_actions.md) + [`AnalyzeDocument`](textract_example_textract_AnalyzeDocument_section.md) + [`DetectDocumentText`](textract_example_textract_DetectDocumentText_section.md) + [`GetDocumentAnalysis`](textract_example_textract_GetDocumentAnalysis_section.md) + [`StartDocumentAnalysis`](textract_example_textract_StartDocumentAnalysis_section.md) + [`StartDocumentTextDetection`](textract_example_textract_StartDocumentTextDetection_section.md) # Actions for Amazon Textract using AWS SDKs The following code examples demonstrate how to perform individual Amazon Textract actions with AWS SDKs. Each example includes a link to GitHub, where you can find instructions for setting up and running the code. These excerpts call the Amazon Textract API and are code excerpts from larger programs that must be run in context. You can see actions in context in [Scenarios for Amazon Textract using AWS SDKs](textract_code_examples_scenarios.md). The following examples include only the most commonly used actions. For a complete list, see the [Amazon Textract API Reference](https://docs.aws.amazon.com/textract/latest/dg/API_Reference.html). **Topics** + [`AnalyzeDocument`](textract_example_textract_AnalyzeDocument_section.md) + [`DetectDocumentText`](textract_example_textract_DetectDocumentText_section.md) + [`GetDocumentAnalysis`](textract_example_textract_GetDocumentAnalysis_section.md) + [`StartDocumentAnalysis`](textract_example_textract_StartDocumentAnalysis_section.md) + [`StartDocumentTextDetection`](textract_example_textract_StartDocumentTextDetection_section.md) # Use `AnalyzeDocument` with an AWS SDK or CLI The following code examples show how to use `AnalyzeDocument`. Action examples are code excerpts from larger programs and must be run in context. You can see this action in context in the following code example: + [Getting started with Amazon Textract](textract_example_s3_GettingStarted_074_section.md) ------ #### [ CLI ] **AWS CLI** **To analyze text in a document** The following `analyze-document` example shows how to analyze text in a document. Linux/macOS: ``` aws textract analyze-document \ --document '{"S3Object":{"Bucket":"bucket","Name":"document"}}' \ --feature-types '["TABLES","FORMS"]' ``` Windows: ``` aws textract analyze-document \ --document "{\"S3Object\":{\"Bucket\":\"bucket\",\"Name\":\"document\"}}" \ --feature-types "[\"TABLES\",\"FORMS\"]" \ --region region-name ``` Output: ``` { "Blocks": [ { "Geometry": { "BoundingBox": { "Width": 1.0, "Top": 0.0, "Left": 0.0, "Height": 1.0 }, "Polygon": [ { "Y": 0.0, "X": 0.0 }, { "Y": 0.0, "X": 1.0 }, { "Y": 1.0, "X": 1.0 }, { "Y": 1.0, "X": 0.0 } ] }, "Relationships": [ { "Type": "CHILD", "Ids": [ "87586964-d50d-43e2-ace5-8a890657b9a0", "a1e72126-21d9-44f4-a8d6-5c385f9002ba", "e889d012-8a6b-4d2e-b7cd-7a8b327d876a" ] } ], "BlockType": "PAGE", "Id": "c2227f12-b25d-4e1f-baea-1ee180d926b2" } ], "DocumentMetadata": { "Pages": 1 } } ``` For more information, see Analyzing Document Text with Amazon Textract in the *Amazon Textract Developers Guide* + For API details, see [AnalyzeDocument](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/textract/analyze-document.html) in *AWS CLI Command Reference*. ------ #### [ Java ] **SDK for Java 2.x** 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/javav2/example_code/textract#code-examples). ``` import software.amazon.awssdk.core.SdkBytes; import software.amazon.awssdk.regions.Region; import software.amazon.awssdk.services.textract.TextractClient; import software.amazon.awssdk.services.textract.model.AnalyzeDocumentRequest; import software.amazon.awssdk.services.textract.model.Document; import software.amazon.awssdk.services.textract.model.FeatureType; import software.amazon.awssdk.services.textract.model.AnalyzeDocumentResponse; import software.amazon.awssdk.services.textract.model.Block; import software.amazon.awssdk.services.textract.model.TextractException; import java.io.File; import java.io.FileInputStream; import java.io.FileNotFoundException; import java.io.InputStream; import java.util.ArrayList; import java.util.Iterator; import java.util.List; /** * Before running this Java V2 code example, set up your development * environment, including your credentials. * * For more information, see the following documentation topic: * * https://docs.aws.amazon.com/sdk-for-java/latest/developer-guide/get-started.html */ public class AnalyzeDocument { public static void main(String[] args) { final String usage = """ Usage: \s Where: sourceDoc - The path where the document is located (must be an image, for example, C:/AWS/book.png).\s """; if (args.length != 1) { System.out.println(usage); System.exit(1); } String sourceDoc = args[0]; Region region = Region.US_EAST_2; TextractClient textractClient = TextractClient.builder() .region(region) .build(); analyzeDoc(textractClient, sourceDoc); textractClient.close(); } public static void analyzeDoc(TextractClient textractClient, String sourceDoc) { try { InputStream sourceStream = new FileInputStream(new File(sourceDoc)); SdkBytes sourceBytes = SdkBytes.fromInputStream(sourceStream); // Get the input Document object as bytes Document myDoc = Document.builder() .bytes(sourceBytes) .build(); List featureTypes = new ArrayList(); featureTypes.add(FeatureType.FORMS); featureTypes.add(FeatureType.TABLES); AnalyzeDocumentRequest analyzeDocumentRequest = AnalyzeDocumentRequest.builder() .featureTypes(featureTypes) .document(myDoc) .build(); AnalyzeDocumentResponse analyzeDocument = textractClient.analyzeDocument(analyzeDocumentRequest); List docInfo = analyzeDocument.blocks(); Iterator blockIterator = docInfo.iterator(); while (blockIterator.hasNext()) { Block block = blockIterator.next(); System.out.println("The block type is " + block.blockType().toString()); } } catch (TextractException | FileNotFoundException e) { System.err.println(e.getMessage()); System.exit(1); } } } ``` + For API details, see [AnalyzeDocument](https://docs.aws.amazon.com/goto/SdkForJavaV2/textract-2018-06-27/AnalyzeDocument) in *AWS SDK for Java 2.x API Reference*. ------ #### [ Python ] **SDK for Python (Boto3)** 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/python/example_code/textract#code-examples). ``` class TextractWrapper: """Encapsulates Textract functions.""" def __init__(self, textract_client, s3_resource, sqs_resource): """ :param textract_client: A Boto3 Textract client. :param s3_resource: A Boto3 Amazon S3 resource. :param sqs_resource: A Boto3 Amazon SQS resource. """ self.textract_client = textract_client self.s3_resource = s3_resource self.sqs_resource = sqs_resource def analyze_file( self, feature_types, *, document_file_name=None, document_bytes=None ): """ Detects text and additional elements, such as forms or tables, in a local image file or from in-memory byte data. The image must be in PNG or JPG format. :param feature_types: The types of additional document features to detect. :param document_file_name: The name of a document image file. :param document_bytes: In-memory byte data of a document image. :return: The response from Amazon Textract, including a list of blocks that describe elements detected in the image. """ if document_file_name is not None: with open(document_file_name, "rb") as document_file: document_bytes = document_file.read() try: response = self.textract_client.analyze_document( Document={"Bytes": document_bytes}, FeatureTypes=feature_types ) logger.info("Detected %s blocks.", len(response["Blocks"])) except ClientError: logger.exception("Couldn't detect text.") raise else: return response ``` + For API details, see [AnalyzeDocument](https://docs.aws.amazon.com/goto/boto3/textract-2018-06-27/AnalyzeDocument) in *AWS SDK for Python (Boto3) API Reference*. ------ #### [ 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). ``` "Detects text and additional elements, such as forms or tables," "in a local image file or from in-memory byte data." "The image must be in PNG or JPG format." "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_document) = NEW /aws1/cl_texdocument( io_s3object = lo_s3object ). "Analyze document stored in Amazon S3." TRY. oo_result = lo_tex->analyzedocument( "oo_result is returned for testing purposes." io_document = lo_document it_featuretypes = lt_featuretypes ). LOOP AT oo_result->get_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. MESSAGE 'Analyze document completed.' 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_texhlquotaexceededex. MESSAGE 'Human loop quota exceeded.' TYPE 'E'. CATCH /aws1/cx_texinternalservererr. MESSAGE 'Internal server error.' 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_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. ``` + For API details, see [AnalyzeDocument](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html) in *AWS SDK for SAP ABAP API reference*. ------ # Use `DetectDocumentText` with an AWS SDK or CLI The following code examples show how to use `DetectDocumentText`. ------ #### [ CLI ] **AWS CLI** **To detect text in a document** The following `detect-document-text` The following example shows how to detect text in a document. Linux/macOS: ``` aws textract detect-document-text \ --document '{"S3Object":{"Bucket":"bucket","Name":"document"}}' ``` Windows: ``` aws textract detect-document-text \ --document "{\"S3Object\":{\"Bucket\":\"bucket\",\"Name\":\"document\"}}" \ --region region-name ``` Output: ``` { "Blocks": [ { "Geometry": { "BoundingBox": { "Width": 1.0, "Top": 0.0, "Left": 0.0, "Height": 1.0 }, "Polygon": [ { "Y": 0.0, "X": 0.0 }, { "Y": 0.0, "X": 1.0 }, { "Y": 1.0, "X": 1.0 }, { "Y": 1.0, "X": 0.0 } ] }, "Relationships": [ { "Type": "CHILD", "Ids": [ "896a9f10-9e70-4412-81ce-49ead73ed881", "0da18623-dc4c-463d-a3d1-9ac050e9e720", "167338d7-d38c-4760-91f1-79a8ec457bb2" ] } ], "BlockType": "PAGE", "Id": "21f0535e-60d5-4bc7-adf2-c05dd851fa25" }, { "Relationships": [ { "Type": "CHILD", "Ids": [ "62490c26-37ea-49fa-8034-7a9ff9369c9c", "1e4f3f21-05bd-4da9-ba10-15d01e66604c" ] } ], "Confidence": 89.11581420898438, "Geometry": { "BoundingBox": { "Width": 0.33642634749412537, "Top": 0.17169663310050964, "Left": 0.13885067403316498, "Height": 0.49159330129623413 }, "Polygon": [ { "Y": 0.17169663310050964, "X": 0.13885067403316498 }, { "Y": 0.17169663310050964, "X": 0.47527703642845154 }, { "Y": 0.6632899641990662, "X": 0.47527703642845154 }, { "Y": 0.6632899641990662, "X": 0.13885067403316498 } ] }, "Text": "He llo,", "BlockType": "LINE", "Id": "896a9f10-9e70-4412-81ce-49ead73ed881" }, { "Relationships": [ { "Type": "CHILD", "Ids": [ "19b28058-9516-4352-b929-64d7cef29daf" ] } ], "Confidence": 85.5694351196289, "Geometry": { "BoundingBox": { "Width": 0.33182239532470703, "Top": 0.23131252825260162, "Left": 0.5091826915740967, "Height": 0.3766750991344452 }, "Polygon": [ { "Y": 0.23131252825260162, "X": 0.5091826915740967 }, { "Y": 0.23131252825260162, "X": 0.8410050868988037 }, { "Y": 0.607987642288208, "X": 0.8410050868988037 }, { "Y": 0.607987642288208, "X": 0.5091826915740967 } ] }, "Text": "worlc", "BlockType": "LINE", "Id": "0da18623-dc4c-463d-a3d1-9ac050e9e720" } ], "DocumentMetadata": { "Pages": 1 } } ``` For more information, see Detecting Document Text with Amazon Textract in the *Amazon Textract Developers Guide* + For API details, see [DetectDocumentText](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/textract/detect-document-text.html) in *AWS CLI Command Reference*. ------ #### [ Java ] **SDK for Java 2.x** 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/javav2/example_code/textract#code-examples). Detect text from an input document. ``` import software.amazon.awssdk.core.SdkBytes; import software.amazon.awssdk.regions.Region; import software.amazon.awssdk.services.textract.TextractClient; import software.amazon.awssdk.services.textract.model.Document; import software.amazon.awssdk.services.textract.model.DetectDocumentTextRequest; import software.amazon.awssdk.services.textract.model.DetectDocumentTextResponse; import software.amazon.awssdk.services.textract.model.Block; import software.amazon.awssdk.services.textract.model.DocumentMetadata; import software.amazon.awssdk.services.textract.model.TextractException; import java.io.File; import java.io.FileInputStream; import java.io.FileNotFoundException; import java.io.InputStream; import java.util.List; /** * Before running this Java V2 code example, set up your development * environment, including your credentials. * * For more information, see the following documentation topic: * * https://docs.aws.amazon.com/sdk-for-java/latest/developer-guide/get-started.html */ public class DetectDocumentText { public static void main(String[] args) { final String usage = """ Usage: \s Where: sourceDoc - The path where the document is located (must be an image, for example, C:/AWS/book.png).\s """; if (args.length != 1) { System.out.println(usage); System.exit(1); } String sourceDoc = args[0]; Region region = Region.US_EAST_2; TextractClient textractClient = TextractClient.builder() .region(region) .build(); detectDocText(textractClient, sourceDoc); textractClient.close(); } public static void detectDocText(TextractClient textractClient, String sourceDoc) { try { InputStream sourceStream = new FileInputStream(new File(sourceDoc)); SdkBytes sourceBytes = SdkBytes.fromInputStream(sourceStream); // Get the input Document object as bytes. Document myDoc = Document.builder() .bytes(sourceBytes) .build(); DetectDocumentTextRequest detectDocumentTextRequest = DetectDocumentTextRequest.builder() .document(myDoc) .build(); // Invoke the Detect operation. DetectDocumentTextResponse textResponse = textractClient.detectDocumentText(detectDocumentTextRequest); List docInfo = textResponse.blocks(); for (Block block : docInfo) { System.out.println("The block type is " + block.blockType().toString()); } DocumentMetadata documentMetadata = textResponse.documentMetadata(); System.out.println("The number of pages in the document is " + documentMetadata.pages()); } catch (TextractException | FileNotFoundException e) { System.err.println(e.getMessage()); System.exit(1); } } } ``` Detect text from a document located in an Amazon S3 bucket. ``` import software.amazon.awssdk.regions.Region; import software.amazon.awssdk.services.textract.model.S3Object; import software.amazon.awssdk.services.textract.TextractClient; import software.amazon.awssdk.services.textract.model.Document; import software.amazon.awssdk.services.textract.model.DetectDocumentTextRequest; import software.amazon.awssdk.services.textract.model.DetectDocumentTextResponse; import software.amazon.awssdk.services.textract.model.Block; import software.amazon.awssdk.services.textract.model.DocumentMetadata; import software.amazon.awssdk.services.textract.model.TextractException; /** * Before running this Java V2 code example, set up your development * environment, including your credentials. * * For more information, see the following documentation topic: * * https://docs.aws.amazon.com/sdk-for-java/latest/developer-guide/get-started.html */ public class DetectDocumentTextS3 { public static void main(String[] args) { final String usage = """ Usage: \s Where: bucketName - The name of the Amazon S3 bucket that contains the document.\s docName - The document name (must be an image, i.e., book.png).\s """; if (args.length != 2) { System.out.println(usage); System.exit(1); } String bucketName = args[0]; String docName = args[1]; Region region = Region.US_WEST_2; TextractClient textractClient = TextractClient.builder() .region(region) .build(); detectDocTextS3(textractClient, bucketName, docName); textractClient.close(); } public static void detectDocTextS3(TextractClient textractClient, String bucketName, String docName) { try { S3Object s3Object = S3Object.builder() .bucket(bucketName) .name(docName) .build(); // Create a Document object and reference the s3Object instance. Document myDoc = Document.builder() .s3Object(s3Object) .build(); DetectDocumentTextRequest detectDocumentTextRequest = DetectDocumentTextRequest.builder() .document(myDoc) .build(); DetectDocumentTextResponse textResponse = textractClient.detectDocumentText(detectDocumentTextRequest); for (Block block : textResponse.blocks()) { System.out.println("The block type is " + block.blockType().toString()); } DocumentMetadata documentMetadata = textResponse.documentMetadata(); System.out.println("The number of pages in the document is " + documentMetadata.pages()); } catch (TextractException e) { System.err.println(e.getMessage()); System.exit(1); } } } ``` + For API details, see [DetectDocumentText](https://docs.aws.amazon.com/goto/SdkForJavaV2/textract-2018-06-27/DetectDocumentText) in *AWS SDK for Java 2.x API Reference*. ------ #### [ Python ] **SDK for Python (Boto3)** 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/python/example_code/textract#code-examples). ``` class TextractWrapper: """Encapsulates Textract functions.""" def __init__(self, textract_client, s3_resource, sqs_resource): """ :param textract_client: A Boto3 Textract client. :param s3_resource: A Boto3 Amazon S3 resource. :param sqs_resource: A Boto3 Amazon SQS resource. """ self.textract_client = textract_client self.s3_resource = s3_resource self.sqs_resource = sqs_resource def detect_file_text(self, *, document_file_name=None, document_bytes=None): """ Detects text elements in a local image file or from in-memory byte data. The image must be in PNG or JPG format. :param document_file_name: The name of a document image file. :param document_bytes: In-memory byte data of a document image. :return: The response from Amazon Textract, including a list of blocks that describe elements detected in the image. """ if document_file_name is not None: with open(document_file_name, "rb") as document_file: document_bytes = document_file.read() try: response = self.textract_client.detect_document_text( Document={"Bytes": document_bytes} ) logger.info("Detected %s blocks.", len(response["Blocks"])) except ClientError: logger.exception("Couldn't detect text.") raise else: return response ``` + For API details, see [DetectDocumentText](https://docs.aws.amazon.com/goto/boto3/textract-2018-06-27/DetectDocumentText) in *AWS SDK for Python (Boto3) API Reference*. ------ #### [ 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). ``` "Detects text in the input document." "Amazon Textract can detect lines of text and the words that make up a line of text." "The input document must be in one of the following image formats: JPEG, PNG, PDF, or TIFF." "Create an ABAP object for the 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_document) = NEW /aws1/cl_texdocument( io_s3object = lo_s3object ). "Analyze document stored in Amazon S3." TRY. oo_result = lo_tex->detectdocumenttext( io_document = lo_document ). "oo_result is returned for testing purposes." LOOP AT oo_result->get_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. DATA(lo_metadata) = oo_result->get_documentmetadata( ). MESSAGE 'The number of pages in the document is ' && lo_metadata->ask_pages( ) TYPE 'I'. MESSAGE 'Detect document text completed.' 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_texinternalservererr. MESSAGE 'Internal server error.' 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_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. ``` + For API details, see [DetectDocumentText](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html) in *AWS SDK for SAP ABAP API reference*. ------ # Use `GetDocumentAnalysis` with an AWS SDK or CLI The following code examples show how to use `GetDocumentAnalysis`. Action examples are code excerpts from larger programs and must be run in context. You can see this action in context in the following code example: + [Get started with document analysis](textract_example_textract_Scenario_GettingStarted_section.md) ------ #### [ CLI ] **AWS CLI** **To get the results of asynchronous text analysis of a multi-page document** The following `get-document-analysis` example shows how to get the results of asynchronous text analysis of a multi-page document. ``` aws textract get-document-analysis \ --job-id df7cf32ebbd2a5de113535fcf4d921926a701b09b4e7d089f3aebadb41e0712b \ --max-results 1000 ``` Output: ``` { "Blocks": [ { "Geometry": { "BoundingBox": { "Width": 1.0, "Top": 0.0, "Left": 0.0, "Height": 1.0 }, "Polygon": [ { "Y": 0.0, "X": 0.0 }, { "Y": 0.0, "X": 1.0 }, { "Y": 1.0, "X": 1.0 }, { "Y": 1.0, "X": 0.0 } ] }, "Relationships": [ { "Type": "CHILD", "Ids": [ "75966e64-81c2-4540-9649-d66ec341cd8f", "bb099c24-8282-464c-a179-8a9fa0a057f0", "5ebf522d-f9e4-4dc7-bfae-a288dc094595" ] } ], "BlockType": "PAGE", "Id": "247c28ee-b63d-4aeb-9af0-5f7ea8ba109e", "Page": 1 } ], "NextToken": "cY1W3eTFvoB0cH7YrKVudI4Gb0H8J0xAYLo8xI/JunCIPWCthaKQ+07n/ElyutsSy0+1VOImoTRmP1zw4P0RFtaeV9Bzhnfedpx1YqwB4xaGDA==", "DocumentMetadata": { "Pages": 1 }, "JobStatus": "SUCCEEDED" } ``` For more information, see Detecting and Analyzing Text in Multi-Page Documents in the *Amazon Textract Developers Guide* + For API details, see [GetDocumentAnalysis](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/textract/get-document-analysis.html) in *AWS CLI Command Reference*. ------ #### [ Python ] **SDK for Python (Boto3)** 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/python/example_code/textract#code-examples). ``` class TextractWrapper: """Encapsulates Textract functions.""" def __init__(self, textract_client, s3_resource, sqs_resource): """ :param textract_client: A Boto3 Textract client. :param s3_resource: A Boto3 Amazon S3 resource. :param sqs_resource: A Boto3 Amazon SQS resource. """ self.textract_client = textract_client self.s3_resource = s3_resource self.sqs_resource = sqs_resource def get_analysis_job(self, job_id): """ Gets data for a previously started detection job that includes additional elements. :param job_id: The ID of the job to retrieve. :return: The job data, including a list of blocks that describe elements detected in the image. """ try: response = self.textract_client.get_document_analysis(JobId=job_id) job_status = response["JobStatus"] logger.info("Job %s status is %s.", job_id, job_status) except ClientError: logger.exception("Couldn't get data for job %s.", job_id) raise else: return response ``` + For API details, see [GetDocumentAnalysis](https://docs.aws.amazon.com/goto/boto3/textract-2018-06-27/GetDocumentAnalysis) in *AWS SDK for Python (Boto3) API Reference*. ------ #### [ 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). ``` "Gets the results for an Amazon Textract" "asynchronous operation that analyzes text in a document." TRY. oo_result = lo_tex->getdocumentanalysis( iv_jobid = iv_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 = iv_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. MESSAGE 'Document analysis retrieved.' TYPE 'I'. CATCH /aws1/cx_texaccessdeniedex. MESSAGE 'You do not have permission to perform this action.' TYPE 'E'. CATCH /aws1/cx_texinternalservererr. MESSAGE 'Internal server error.' TYPE 'E'. CATCH /aws1/cx_texinvalidjobidex. MESSAGE 'Job ID is not valid.' 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_texprovthruputexcdex. MESSAGE 'Provisioned throughput exceeded limit.' TYPE 'E'. CATCH /aws1/cx_texthrottlingex. MESSAGE 'The request processing exceeded the limit.' TYPE 'E'. ENDTRY. ``` + For API details, see [GetDocumentAnalysis](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html) in *AWS SDK for SAP ABAP API reference*. ------ # Use `StartDocumentAnalysis` with an AWS SDK or CLI The following code examples show how to use `StartDocumentAnalysis`. Action examples are code excerpts from larger programs and must be run in context. You can see this action in context in the following code example: + [Get started with document analysis](textract_example_textract_Scenario_GettingStarted_section.md) ------ #### [ CLI ] **AWS CLI** **To start analyzing text in a multi-page document** The following `start-document-analysis` example shows how to start asynchronous analysis of text in a multi-page document. Linux/macOS: ``` aws textract start-document-analysis \ --document-location '{"S3Object":{"Bucket":"bucket","Name":"document"}}' \ --feature-types '["TABLES","FORMS"]' \ --notification-channel "SNSTopicArn=arn:snsTopic,RoleArn=roleArn" ``` Windows: ``` aws textract start-document-analysis \ --document-location "{\"S3Object\":{\"Bucket\":\"bucket\",\"Name\":\"document\"}}" \ --feature-types "[\"TABLES\", \"FORMS\"]" \ --region region-name \ --notification-channel "SNSTopicArn=arn:snsTopic,RoleArn=roleArn" ``` Output: ``` { "JobId": "df7cf32ebbd2a5de113535fcf4d921926a701b09b4e7d089f3aebadb41e0712b" } ``` For more information, see Detecting and Analyzing Text in Multi-Page Documents in the *Amazon Textract Developers Guide* + For API details, see [StartDocumentAnalysis](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/textract/start-document-analysis.html) in *AWS CLI Command Reference*. ------ #### [ Java ] **SDK for Java 2.x** 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/javav2/example_code/textract#code-examples). ``` import software.amazon.awssdk.regions.Region; import software.amazon.awssdk.services.textract.model.S3Object; import software.amazon.awssdk.services.textract.TextractClient; import software.amazon.awssdk.services.textract.model.StartDocumentAnalysisRequest; import software.amazon.awssdk.services.textract.model.DocumentLocation; import software.amazon.awssdk.services.textract.model.TextractException; import software.amazon.awssdk.services.textract.model.StartDocumentAnalysisResponse; import software.amazon.awssdk.services.textract.model.GetDocumentAnalysisRequest; import software.amazon.awssdk.services.textract.model.GetDocumentAnalysisResponse; import software.amazon.awssdk.services.textract.model.FeatureType; import java.util.ArrayList; import java.util.List; /** * Before running this Java V2 code example, set up your development * environment, including your credentials. * * For more information, see the following documentation topic: * * https://docs.aws.amazon.com/sdk-for-java/latest/developer-guide/get-started.html */ public class StartDocumentAnalysis { public static void main(String[] args) { final String usage = """ Usage: \s Where: bucketName - The name of the Amazon S3 bucket that contains the document.\s docName - The document name (must be an image, for example, book.png).\s """; if (args.length != 2) { System.out.println(usage); System.exit(1); } String bucketName = args[0]; String docName = args[1]; Region region = Region.US_WEST_2; TextractClient textractClient = TextractClient.builder() .region(region) .build(); String jobId = startDocAnalysisS3(textractClient, bucketName, docName); System.out.println("Getting results for job " + jobId); String status = getJobResults(textractClient, jobId); System.out.println("The job status is " + status); textractClient.close(); } public static String startDocAnalysisS3(TextractClient textractClient, String bucketName, String docName) { try { List myList = new ArrayList<>(); myList.add(FeatureType.TABLES); myList.add(FeatureType.FORMS); S3Object s3Object = S3Object.builder() .bucket(bucketName) .name(docName) .build(); DocumentLocation location = DocumentLocation.builder() .s3Object(s3Object) .build(); StartDocumentAnalysisRequest documentAnalysisRequest = StartDocumentAnalysisRequest.builder() .documentLocation(location) .featureTypes(myList) .build(); StartDocumentAnalysisResponse response = textractClient.startDocumentAnalysis(documentAnalysisRequest); // Get the job ID String jobId = response.jobId(); return jobId; } catch (TextractException e) { System.err.println(e.getMessage()); System.exit(1); } return ""; } private static String getJobResults(TextractClient textractClient, String jobId) { boolean finished = false; int index = 0; String status = ""; try { while (!finished) { GetDocumentAnalysisRequest analysisRequest = GetDocumentAnalysisRequest.builder() .jobId(jobId) .maxResults(1000) .build(); GetDocumentAnalysisResponse response = textractClient.getDocumentAnalysis(analysisRequest); status = response.jobStatus().toString(); if (status.compareTo("SUCCEEDED") == 0) finished = true; else { System.out.println(index + " status is: " + status); Thread.sleep(1000); } index++; } return status; } catch (InterruptedException e) { System.out.println(e.getMessage()); System.exit(1); } return ""; } } ``` + For API details, see [StartDocumentAnalysis](https://docs.aws.amazon.com/goto/SdkForJavaV2/textract-2018-06-27/StartDocumentAnalysis) in *AWS SDK for Java 2.x API Reference*. ------ #### [ Python ] **SDK for Python (Boto3)** 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/python/example_code/textract#code-examples). Start an asynchronous job to analyze a document. ``` class TextractWrapper: """Encapsulates Textract functions.""" def __init__(self, textract_client, s3_resource, sqs_resource): """ :param textract_client: A Boto3 Textract client. :param s3_resource: A Boto3 Amazon S3 resource. :param sqs_resource: A Boto3 Amazon SQS resource. """ self.textract_client = textract_client self.s3_resource = s3_resource self.sqs_resource = sqs_resource def start_analysis_job( self, bucket_name, document_file_name, feature_types, sns_topic_arn, sns_role_arn, ): """ Starts an asynchronous job to detect text and additional elements, such as forms or tables, in an image stored in an Amazon S3 bucket. Textract publishes a notification to the specified Amazon SNS topic when the job completes. The image must be in PNG, JPG, or PDF format. :param bucket_name: The name of the Amazon S3 bucket that contains the image. :param document_file_name: The name of the document image stored in Amazon S3. :param feature_types: The types of additional document features to detect. :param sns_topic_arn: The Amazon Resource Name (ARN) of an Amazon SNS topic where job completion notification is published. :param sns_role_arn: The ARN of an AWS Identity and Access Management (IAM) role that can be assumed by Textract and grants permission to publish to the Amazon SNS topic. :return: The ID of the job. """ try: response = self.textract_client.start_document_analysis( DocumentLocation={ "S3Object": {"Bucket": bucket_name, "Name": document_file_name} }, NotificationChannel={ "SNSTopicArn": sns_topic_arn, "RoleArn": sns_role_arn, }, FeatureTypes=feature_types, ) job_id = response["JobId"] logger.info( "Started text analysis job %s on %s.", job_id, document_file_name ) except ClientError: logger.exception("Couldn't analyze text in %s.", document_file_name) raise else: return job_id ``` + For API details, see [StartDocumentAnalysis](https://docs.aws.amazon.com/goto/boto3/textract-2018-06-27/StartDocumentAnalysis) in *AWS SDK for Python (Boto3) API Reference*. ------ #### [ 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). ``` "Starts the asynchronous analysis of an input document for relationships" "between detected items such as key-value pairs, tables, and selection elements." "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 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 async document analysis." TRY. oo_result = lo_tex->startdocumentanalysis( "oo_result is returned for testing purposes." io_documentlocation = lo_documentlocation it_featuretypes = lt_featuretypes ). DATA(lv_jobid) = oo_result->get_jobid( ). 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. ``` + For API details, see [StartDocumentAnalysis](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html) in *AWS SDK for SAP ABAP API reference*. ------ # Use `StartDocumentTextDetection` with an AWS SDK or CLI The following code examples show how to use `StartDocumentTextDetection`. ------ #### [ CLI ] **AWS CLI** **To start detecting text in a multi-page document** The following `start-document-text-detection` example shows how to start asynchronous detection of text in a multi-page document. Linux/macOS: ``` aws textract start-document-text-detection \ --document-location '{"S3Object":{"Bucket":"bucket","Name":"document"}}' \ --notification-channel "SNSTopicArn=arn:snsTopic,RoleArn=roleARN" ``` Windows: ``` aws textract start-document-text-detection \ --document-location "{\"S3Object\":{\"Bucket\":\"bucket\",\"Name\":\"document\"}}" \ --region region-name \ --notification-channel "SNSTopicArn=arn:snsTopic,RoleArn=roleArn" ``` Output: ``` { "JobId": "57849a3dc627d4df74123dca269d69f7b89329c870c65bb16c9fd63409d200b9" } ``` For more information, see Detecting and Analyzing Text in Multi-Page Documents in the *Amazon Textract Developers Guide* + For API details, see [StartDocumentTextDetection](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/textract/start-document-text-detection.html) in *AWS CLI Command Reference*. ------ #### [ Python ] **SDK for Python (Boto3)** 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/python/example_code/textract#code-examples). Start an asynchronous job to detect text in a document. ``` class TextractWrapper: """Encapsulates Textract functions.""" def __init__(self, textract_client, s3_resource, sqs_resource): """ :param textract_client: A Boto3 Textract client. :param s3_resource: A Boto3 Amazon S3 resource. :param sqs_resource: A Boto3 Amazon SQS resource. """ self.textract_client = textract_client self.s3_resource = s3_resource self.sqs_resource = sqs_resource def start_detection_job( self, bucket_name, document_file_name, sns_topic_arn, sns_role_arn ): """ Starts an asynchronous job to detect text elements in an image stored in an Amazon S3 bucket. Textract publishes a notification to the specified Amazon SNS topic when the job completes. The image must be in PNG, JPG, or PDF format. :param bucket_name: The name of the Amazon S3 bucket that contains the image. :param document_file_name: The name of the document image stored in Amazon S3. :param sns_topic_arn: The Amazon Resource Name (ARN) of an Amazon SNS topic where the job completion notification is published. :param sns_role_arn: The ARN of an AWS Identity and Access Management (IAM) role that can be assumed by Textract and grants permission to publish to the Amazon SNS topic. :return: The ID of the job. """ try: response = self.textract_client.start_document_text_detection( DocumentLocation={ "S3Object": {"Bucket": bucket_name, "Name": document_file_name} }, NotificationChannel={ "SNSTopicArn": sns_topic_arn, "RoleArn": sns_role_arn, }, ) job_id = response["JobId"] logger.info( "Started text detection job %s on %s.", job_id, document_file_name ) except ClientError: logger.exception("Couldn't detect text in %s.", document_file_name) raise else: return job_id ``` + For API details, see [StartDocumentTextDetection](https://docs.aws.amazon.com/goto/boto3/textract-2018-06-27/StartDocumentTextDetection) in *AWS SDK for Python (Boto3) API Reference*. ------ #### [ 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). ``` "Starts the asynchronous detection of text in a document." "Amazon Textract can detect lines of text and the words that make up a line of text." "Create an ABAP object for the 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. oo_result = lo_tex->startdocumenttextdetection( io_documentlocation = lo_documentlocation ). DATA(lv_jobid) = oo_result->get_jobid( ). "oo_result is returned for testing purposes." 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. ``` + For API details, see [StartDocumentTextDetection](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html) in *AWS SDK for SAP ABAP API reference*. ------ # 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) ------