文件 AWS 開發套件範例 GitHub 儲存庫中有更多可用的 [AWS SDK 範例](https://github.com/awsdocs/aws-doc-sdk-examples)。 本文為英文版的機器翻譯版本，如內容有任何歧義或不一致之處，概以英文版為準。 # 使用 AWS SDKs 的 Amazon Textract 程式碼範例 下列程式碼範例示範如何使用 Amazon Textract 搭配 AWS 軟體開發套件 (SDK)。 *Actions* 是大型程式的程式碼摘錄，必須在內容中執行。雖然動作會告訴您如何呼叫個別服務函數，但您可以在其相關情境中查看內容中的動作。 *案例*是向您展示如何呼叫服務中的多個函數或與其他 AWS 服務組合來完成特定任務的程式碼範例。 **其他資源** + **[Amazon Textract 開發人員指南](https://docs.aws.amazon.com/textract/latest/dg/what-is.html)** – Amazon Textract 的詳細資訊。 + **[Amazon Textract API 參考](https://docs.aws.amazon.com/textract/latest/dg/API_Reference.html)** – 所有可用 Amazon Textract 動作的詳細資訊。 + **[AWS 開發人員中心](https://aws.amazon.com/developer/code-examples/?awsf.sdk-code-examples-product=product%23textract)** – 您可以依類別或全文搜尋篩選的程式碼範例。 + **[AWS SDK 範例](https://github.com/awsdocs/aws-doc-sdk-examples)** – GitHub 儲存庫使用慣用語言的完整程式碼。包含設定和執行程式碼的指示。 **Contents** + [基本概念](textract_code_examples_basics.md) + [動作](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) + [案例](textract_code_examples_scenarios.md) + [建立 Amazon Textract Explorer 應用程式](textract_example_cross_TextractExplorer_section.md) + [建立應用程式以分析客戶意見回饋](textract_example_cross_FSA_section.md) + [偵測從影像擷取的文字中的實體](textract_example_cross_TextractComprehendDetectEntities_section.md) + [開始使用文件分析](textract_example_textract_Scenario_GettingStarted_section.md) # 使用 AWS SDKs 的 Amazon Textract 基本範例 下列程式碼範例示範如何搭配 AWS SDK 使用 Amazon Textract 的基本功能。 **Contents** + [動作](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) # 使用 AWS SDKs 的 Amazon Textract 動作 下列程式碼範例示範如何使用 AWS SDKs 執行個別 Amazon Textract 動作。每個範例均包含 GitHub 的連結，您可以在連結中找到設定和執行程式碼的相關說明。 這些摘錄會呼叫 Amazon Textract API，是必須在內容中執行之大型程式的程式碼摘錄。您可以在 [使用 AWS SDKs Amazon Textract 案例](textract_code_examples_scenarios.md) 中查看內容中的動作。 下列範例僅包含最常使用的動作。如需完整清單，請參閱《[Amazon Textract API 參考](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) # `AnalyzeDocument` 搭配 AWS SDK 或 CLI 使用 下列程式碼範例示範如何使用 `AnalyzeDocument`。 ------ #### [ CLI ] **AWS CLI** **分析文件中的文字** 下列 `analyze-document` 範例示範如何分析文件中的文字。 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 ``` 輸出： ``` { "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 } } ``` 如需詳細資訊，請參閱《Amazon Textract 開發人員指南》**中的「使用 Amazon Textract 分析文件文字」 + 如需 API 詳細資訊，請參閱《AWS CLI 命令參考》**中的 [AnalyzeDocument](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/textract/analyze-document.html)。 ------ #### [ Java ] **SDK for Java 2.x** GitHub 上提供更多範例。尋找完整範例，並了解如何在 [AWS 程式碼範例儲存庫](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javav2/example_code/textract#code-examples)中設定和執行。 ``` 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); } } } ``` + 如需 API 詳細資訊，請參閱《AWS SDK for Java 2.x API 參考》**中的 [AnalyzeDocument](https://docs.aws.amazon.com/goto/SdkForJavaV2/textract-2018-06-27/AnalyzeDocument)。 ------ #### [ Python ] **適用於 Python 的 SDK (Boto3)** GitHub 上提供更多範例。尋找完整範例，並了解如何在 [AWS 程式碼範例儲存庫](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/textract#code-examples)中設定和執行。 ``` 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 ``` + 如需 API 詳細資訊，請參閱《AWS SDK for Python (Boto3) API 參考》**中的 [AnalyzeDocument](https://docs.aws.amazon.com/goto/boto3/textract-2018-06-27/AnalyzeDocument)。 ------ #### [ SAP ABAP ] **適用於 SAP ABAP 的開發套件** GitHub 上提供更多範例。尋找完整範例，並了解如何在 [AWS 程式碼範例儲存庫](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/tex#code-examples)中設定和執行。 ``` "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. ``` + 如需 API 詳細資訊，請參閱《適用於 SAP ABAP 的 AWS  SDK API 參考》**中的 [AnalyzeDocument](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)。 ------ # `DetectDocumentText` 搭配 AWS SDK 或 CLI 使用 下列程式碼範例示範如何使用 `DetectDocumentText`。 ------ #### [ CLI ] **AWS CLI** **偵測文件中的文字** 下列 `detect-document-text` 範例示範如何偵測文件中的文字。 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 ``` 輸出： ``` { "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 } } ``` 如需詳細資訊，請參閱《Amazon Textract 開發人員指南》**中的「使用 Amazon Textract 偵測文件文字」 + 如需 API 詳細資訊，請參閱《AWS CLI 命令參考》**中的 [DetectDocumentText](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/textract/detect-document-text.html)。 ------ #### [ Java ] **SDK for Java 2.x** GitHub 上提供更多範例。尋找完整範例，並了解如何在 [AWS 程式碼範例儲存庫](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javav2/example_code/textract#code-examples)中設定和執行。 從輸入文件偵測文字。 ``` 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); } } } ``` 從位於 Amazon S3 儲存貯體的文件偵測文字。 ``` 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); } } } ``` + 如需 API 詳細資訊，請參閱《AWS SDK for Java 2.x API 參考》**中的 [DetectDocumentText](https://docs.aws.amazon.com/goto/SdkForJavaV2/textract-2018-06-27/DetectDocumentText)。 ------ #### [ Python ] **適用於 Python 的 SDK (Boto3)** GitHub 上提供更多範例。尋找完整範例，並了解如何在 [AWS 程式碼範例儲存庫](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/textract#code-examples)中設定和執行。 ``` 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 ``` + 如需 API 詳細資訊，請參閱《AWS SDK for Python (Boto3) API 參考》**中的 [DetectDocumentText](https://docs.aws.amazon.com/goto/boto3/textract-2018-06-27/DetectDocumentText)。 ------ #### [ SAP ABAP ] **適用於 SAP ABAP 的開發套件** GitHub 上提供更多範例。尋找完整範例，並了解如何在 [AWS 程式碼範例儲存庫](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/tex#code-examples)中設定和執行。 ``` "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. ``` + 如需 API 詳細資訊，請參閱《適用於 SAP ABAP 的AWS SDK API 參考》**中的 [DetectDocumentText](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)。 ------ # `GetDocumentAnalysis` 搭配 AWS SDK 或 CLI 使用 下列程式碼範例示範如何使用 `GetDocumentAnalysis`。 動作範例是大型程式的程式碼摘錄，必須在內容中執行。您可以在下列程式碼範例的內容中看到此動作： + [開始使用文件分析](textract_example_textract_Scenario_GettingStarted_section.md) ------ #### [ CLI ] **AWS CLI** **取得多頁文件的非同步文字分析結果** 下列 `get-document-analysis` 範例會示範如何取得多頁文件的非同步文字分析結果。 ``` aws textract get-document-analysis \ --job-id df7cf32ebbd2a5de113535fcf4d921926a701b09b4e7d089f3aebadb41e0712b \ --max-results 1000 ``` 輸出： ``` { "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" } ``` 如需詳細資訊，請參閱《Amazon Textract 開發人員指南》**中的「偵測和分析多頁文件中的文字」 + 如需 API 詳細資訊，請參閱《AWS CLI 命令參考》**中的 [GetDocumentAnalysis](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/textract/get-document-analysis.html)。 ------ #### [ Python ] **適用於 Python 的 SDK (Boto3)** GitHub 上提供更多範例。尋找完整範例，並了解如何在 [AWS 程式碼範例儲存庫](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/textract#code-examples)中設定和執行。 ``` 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 ``` + 如需 API 詳細資訊，請參閱《AWS SDK for Python (Boto3) API 參考》**中的 [GetDocumentAnalysis](https://docs.aws.amazon.com/goto/boto3/textract-2018-06-27/GetDocumentAnalysis)。 ------ #### [ SAP ABAP ] **適用於 SAP ABAP 的開發套件** GitHub 上提供更多範例。尋找完整範例，並了解如何在 [AWS 程式碼範例儲存庫](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/tex#code-examples)中設定和執行。 ``` "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. ``` + 如需 API 詳細資訊，請參閱《適用於 SAP ABAP 的AWS SDK API 參考》**中的 [GetDocumentAnalysis](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)。 ------ # `StartDocumentAnalysis` 搭配 AWS SDK 或 CLI 使用 下列程式碼範例示範如何使用 `StartDocumentAnalysis`。 動作範例是大型程式的程式碼摘錄，必須在內容中執行。您可以在下列程式碼範例的內容中看到此動作： + [開始使用文件分析](textract_example_textract_Scenario_GettingStarted_section.md) ------ #### [ CLI ] **AWS CLI** **開始分析多頁文件中的文字** 下列 `start-document-analysis` 範例會示範如何開始多頁文件文字的非同步分析。 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" ``` 輸出： ``` { "JobId": "df7cf32ebbd2a5de113535fcf4d921926a701b09b4e7d089f3aebadb41e0712b" } ``` 如需詳細資訊，請參閱《Amazon Textract 開發人員指南》**中的「偵測和分析多頁文件中的文字」 + 如需 API 詳細資訊，請參閱《AWS CLI 命令參考》**中的 [StartDocumentAnalysis](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/textract/start-document-analysis.html)。 ------ #### [ Java ] **SDK for Java 2.x** GitHub 上提供更多範例。尋找完整範例，並了解如何在 [AWS 程式碼範例儲存庫](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javav2/example_code/textract#code-examples)中設定和執行。 ``` 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 ""; } } ``` + 如需 API 詳細資訊，請參閱《AWS SDK for Java 2.x API 參考》**中的 [StartDocumentAnalysis](https://docs.aws.amazon.com/goto/SdkForJavaV2/textract-2018-06-27/StartDocumentAnalysis)。 ------ #### [ Python ] **適用於 Python 的 SDK (Boto3)** GitHub 上提供更多範例。尋找完整範例，並了解如何在 [AWS 程式碼範例儲存庫](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/textract#code-examples)中設定和執行。 啟動非同步任務以分析文件。 ``` 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 ``` + 如需 API 詳細資訊，請參閱《AWS SDK for Python (Boto3) API 參考》**中的 [StartDocumentAnalysis](https://docs.aws.amazon.com/goto/boto3/textract-2018-06-27/StartDocumentAnalysis)。 ------ #### [ SAP ABAP ] **適用於 SAP ABAP 的開發套件** GitHub 上提供更多範例。尋找完整範例，並了解如何在 [AWS 程式碼範例儲存庫](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/tex#code-examples)中設定和執行。 ``` "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. ``` + 如需 API 詳細資訊，請參閱《適用於 SAP ABAP 的 AWS  SDK API 參考》**中的 [StartDocumentAnalysis](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)。 ------ # `StartDocumentTextDetection` 搭配 AWS SDK 或 CLI 使用 下列程式碼範例示範如何使用 `StartDocumentTextDetection`。 ------ #### [ CLI ] **AWS CLI** **開始偵測多頁文件中的文字** 下列 `start-document-text-detection` 範例會示範如何開始多頁文件文字的非同步偵測。 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" ``` 輸出： ``` { "JobId": "57849a3dc627d4df74123dca269d69f7b89329c870c65bb16c9fd63409d200b9" } ``` 如需詳細資訊，請參閱《Amazon Textract 開發人員指南》**中的「偵測和分析多頁文件中的文字」 + 如需 API 詳細資訊，請參閱《AWS CLI 命令參考》**中的 [StartDocumentTextDetection](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/textract/start-document-text-detection.html)。 ------ #### [ Python ] **適用於 Python 的 SDK (Boto3)** GitHub 上提供更多範例。尋找完整範例，並了解如何在 [AWS 程式碼範例儲存庫](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/textract#code-examples)中設定和執行。 啟動非同步任務，以偵測文件中的文字。 ``` 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 ``` + 如需 API 詳細資訊，請參閱《AWS SDK for Python (Boto3) API 參考》**中的 [StartDocumentTextDetection](https://docs.aws.amazon.com/goto/boto3/textract-2018-06-27/StartDocumentTextDetection)。 ------ #### [ SAP ABAP ] **適用於 SAP ABAP 的開發套件** GitHub 上提供更多範例。尋找完整範例，並了解如何在 [AWS 程式碼範例儲存庫](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/tex#code-examples)中設定和執行。 ``` "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. ``` + 如需 API 詳細資訊，請參閱《適用於 SAP ABAP 的 AWS  SDK API 參考》**中的 [StartDocumentTextDetection](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)。 ------ # 使用 AWS SDKs Amazon Textract 案例 下列程式碼範例示範如何在 Amazon Textract AWS SDKs 中實作常見案例。這些案例示範如何呼叫 Amazon Textract 中的多個函數，或與其他 AWS 服務結合，藉以完成特定任務。每個案例均包含完整原始碼的連結，您可在連結中找到如何設定和執行程式碼的相關指示。 案例的目標是獲得中等水平的經驗，協助您了解內容中的服務動作。 **Topics** + [建立 Amazon Textract Explorer 應用程式](textract_example_cross_TextractExplorer_section.md) + [建立應用程式以分析客戶意見回饋](textract_example_cross_FSA_section.md) + [偵測從影像擷取的文字中的實體](textract_example_cross_TextractComprehendDetectEntities_section.md) + [開始使用文件分析](textract_example_textract_Scenario_GettingStarted_section.md) # 建立 Amazon Textract Explorer 應用程式 下列程式碼範例示範如何透過互動式應用程式探索 Amazon Textract 輸出。 ------ #### [ JavaScript ] **適用於 JavaScript (v3) 的 SDK** 示範如何使用 適用於 JavaScript 的 AWS SDK 建置 React 應用程式，該應用程式使用 Amazon Textract 從文件映像擷取資料，並將其顯示在互動式網頁中。此範例會在 Web 瀏覽器中執行，且登入資料需要經過驗證的 Amazon Cognito 身分。它使用 Amazon Simple Storage Service (Amazon S3 進行儲存，對於通知，它會輪詢訂閱 Amazon Simple Notification Service (Amazon SNS)) 主題的 Amazon Simple Queue Service (Amazon SQS) 佇列。 如需完整的原始碼和如何設定及執行的指示，請參閱 [GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javascriptv3/example_code/cross-services/textract-react) 上的完整範例。 **此範例中使用的服務** + Amazon Cognito Identity + Amazon S3 + Amazon SNS + Amazon SQS + Amazon Textract ------ #### [ Python ] **適用於 Python 的 SDK (Boto3)** 顯示如何使用 適用於 Python (Boto3) 的 AWS SDK 搭配 Amazon Textract 來偵測文件映像中的文字、表單和資料表元素。輸入影像和 Amazon Textract 輸出會顯示在 Tkinter 應用程式中，可讓您探索偵測到的元素。 + 將文件影像提交到 Amazon Textract，並探索偵測到元素的輸出。 + 將影像直接傳送至 Amazon Textract 或透過 Amazon Simple Storage Service (Amazon S3) 儲存貯體。 + 使用非同步 API 可以在任務完成時啟動將通知發布到 Amazon Simple Notification Service (Amazon SNS) 主題的任務。 + 輪詢 Amazon Simple Queue Service (Amazon SQS) 佇列以取得任務完成訊息並顯示結果。 如需完整的原始碼和如何設定及執行的指示，請參閱 [GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/cross_service/textract_explorer) 上的完整範例。 **此範例中使用的服務** + Amazon Cognito Identity + Amazon S3 + Amazon SNS + Amazon SQS + Amazon Textract ------ # 建立可分析客戶意見回饋並合成音訊的應用程式 下列程式碼範例示範如何建立應用程式，以分析客戶評論卡、從其原始語言進行翻譯、判斷對方情緒，以及透過翻譯後的文字產生音訊檔案。 ------ #### [ .NET ] **適用於 .NET 的 SDK** 此範例應用程式會分析和存儲客戶的意見回饋卡。具體來說，它滿足了紐約市一家虛構飯店的需求。飯店以實體評論卡的形式收到賓客以各種語言撰寫的意見回饋。這些意見回饋透過 Web 用戶端上傳至應用程式。評論卡的影像上傳後，系統會執行下列步驟： + 文字內容是使用 Amazon Textract 從影像中擷取。 + Amazon Comprehend 會決定擷取文字及其用語的情感。 + 擷取的文字內容會使用 Amazon Translate 翻譯成英文。 + Amazon Polly 會使用擷取的文字內容合成音訊檔案。 完整的應用程式可透過  AWS CDK 部署。如需原始程式碼和部署的說明，請參閱 [GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/dotnetv3/cross-service/FeedbackSentimentAnalyzer) 中的專案。 **此範例中使用的服務** + Amazon Comprehend + Lambda + Amazon Polly + Amazon Textract + Amazon Translate ------ #### [ Java ] **適用於 Java 2.x 的 SDK** 此範例應用程式會分析和存儲客戶的意見回饋卡。具體來說，它滿足了紐約市一家虛構飯店的需求。飯店以實體評論卡的形式收到賓客以各種語言撰寫的意見回饋。這些意見回饋透過 Web 用戶端上傳至應用程式。評論卡的影像上傳後，系統會執行下列步驟： + 文字內容是使用 Amazon Textract 從影像中擷取。 + Amazon Comprehend 會決定擷取文字及其用語的情感。 + 擷取的文字內容會使用 Amazon Translate 翻譯成英文。 + Amazon Polly 會使用擷取的文字內容合成音訊檔案。 完整的應用程式可透過  AWS CDK 部署。如需原始程式碼和部署的說明，請參閱 [GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javav2/usecases/creating_fsa_app) 中的專案。 **此範例中使用的服務** + Amazon Comprehend + Lambda + Amazon Polly + Amazon Textract + Amazon Translate ------ #### [ JavaScript ] **適用於 JavaScript (v3) 的 SDK** 此範例應用程式會分析和存儲客戶的意見回饋卡。具體來說，它滿足了紐約市一家虛構飯店的需求。飯店以實體評論卡的形式收到賓客以各種語言撰寫的意見回饋。這些意見回饋透過 Web 用戶端上傳至應用程式。評論卡的影像上傳後，系統會執行下列步驟： + 文字內容是使用 Amazon Textract 從影像中擷取。 + Amazon Comprehend 會決定擷取文字及其用語的情感。 + 擷取的文字內容會使用 Amazon Translate 翻譯成英文。 + Amazon Polly 會使用擷取的文字內容合成音訊檔案。 完整的應用程式可透過  AWS CDK 部署。如需原始程式碼和部署的說明，請參閱 [GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javascriptv3/example_code/cross-services/feedback-sentiment-analyzer) 中的專案。以下摘錄顯示如何在 Lambda 函數內 適用於 JavaScript 的 AWS SDK 使用 。 ``` 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 }; }; ``` **此範例中使用的服務** + Amazon Comprehend + Lambda + Amazon Polly + Amazon Textract + Amazon Translate ------ #### [ Ruby ] **SDK for Ruby** 此範例應用程式會分析和存儲客戶的意見回饋卡。具體來說，它滿足了紐約市一家虛構飯店的需求。飯店以實體評論卡的形式收到賓客以各種語言撰寫的意見回饋。這些意見回饋透過 Web 用戶端上傳至應用程式。評論卡的影像上傳後，系統會執行下列步驟： + 文字內容是使用 Amazon Textract 從影像中擷取。 + Amazon Comprehend 會決定擷取文字及其用語的情感。 + 擷取的文字內容會使用 Amazon Translate 翻譯成英文。 + Amazon Polly 會使用擷取的文字內容合成音訊檔案。 完整的應用程式可透過  AWS CDK 部署。如需原始程式碼和部署的說明，請參閱 [GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/ruby/cross_service_examples/feedback_sentiment_analyzer) 中的專案。 **此範例中使用的服務** + Amazon Comprehend + Lambda + Amazon Polly + Amazon Textract + Amazon Translate ------ # 使用 AWS SDK 偵測從映像擷取的文字中的實體 下列程式碼範例示範如何使用 Amazon Comprehend 偵測 Amazon Textract 從存放在 Amazon S3 中的影像中提取的文字中的實體。 ------ #### [ Python ] **適用於 Python 的 SDK (Boto3)** 顯示如何使用 Jupyter 筆記本 適用於 Python (Boto3) 的 AWS SDK 中的 來偵測從影像擷取的文字中的實體。本範例使用 Amazon Textract 從儲存於 Amazon Simple Storage Service (Amazon S3) 和 Amazon Comprehend 中的影像提取文字，以偵測擷取文字中的實體。 此範例是 Jupyter 的筆記型電腦，必須在可以託管的筆記型電腦的環境中運行。如需使用 Amazon SageMaker AI 執行範例的指示，請參閱 [TextractAndComprehendNotebook.ipynb](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/cross_service/textract_comprehend_notebook/TextractAndComprehendNotebook.ipynb) 中的說明。 如需完整的原始碼和如何設定及執行的指示，請參閱 [GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/cross_service/textract_comprehend_notebook#readme) 上的完整範例。 **此範例中使用的服務** + Amazon Comprehend + Amazon S3 + Amazon Textract ------ # 使用 AWS SDK 開始使用 Amazon Textract 文件分析 以下程式碼範例顯示做法： + 開始非同步分析。 + 取得文件分析。 ------ #### [ SAP ABAP ] **適用於 SAP ABAP 的開發套件** GitHub 上提供更多範例。尋找完整範例，並了解如何在 [AWS 程式碼範例儲存庫](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/tex#code-examples)中設定和執行。 ``` "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. ``` + 如需 API 詳細資訊，請參閱《*適用於 SAP ABAP 的AWS SDK API 參考*》中的下列主題。 + [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) ------