文档 AWS SDK 示例 GitHub 存储库中还有更多 [S AWS DK 示例](https://github.com/awsdocs/aws-doc-sdk-examples)。 本文属于机器翻译版本。若本译文内容与英语原文存在差异，则一律以英文原文为准。 # 使用 Amazon Textract 的代码示例 AWS SDKs 以下代码示例向您展示了如何将 Amazon Textract 与 AWS 软件开发套件 (SDK) 一起使用。 *操作*是大型程序的代码摘录，必须在上下文中运行。您可以通过操作了解如何调用单个服务函数，还可以通过函数相关场景的上下文查看操作。 *场景*是向您展示如何通过在一个服务中调用多个函数或与其他 AWS 服务服务结合来完成特定任务的代码示例。 **更多资源** + **[Amazon Textract 开发人员指南](https://docs.aws.amazon.com/textract/latest/dg/what-is.html)**——有关 Amazon Textract 的更多信息。 + **[Amazon Textract API 参考](https://docs.aws.amazon.com/textract/latest/dg/API_Reference.html)**——有关所有可用的 Amazon Textract 操作的详细信息。 + **[AWS 开发者中心](https://aws.amazon.com/developer/code-examples/?awsf.sdk-code-examples-product=product%23textract)** — 您可以按类别或全文搜索筛选的代码示例。 + **[AWS SDK 示例](https://github.com/awsdocs/aws-doc-sdk-examples)** — 包含首选语言完整代码的 GitHub 存储库。包括有关设置和运行代码的说明。 **Contents** + [基本功能](textract_code_examples_basics.md) + [操作](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) + [场景](textract_code_examples_scenarios.md) + [创建 Amazon Textract 浏览器应用程序](textract_example_cross_TextractExplorer_section.md) + [创建用于分析客户反馈的应用程序](textract_example_cross_FSA_section.md) + [检测从图像中提取的文本中的实体](textract_example_cross_TextractComprehendDetectEntities_section.md) + [开始使用文档分析](textract_example_textract_Scenario_GettingStarted_section.md) # 使用 Amazon Textract 的基本示例 AWS SDKs 以下代码示例展示了如何使用 Amazon Textract 的基础知识。 AWS SDKs **Contents** + [操作](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 Amazon Textract 执行的操作 AWS SDKs 以下代码示例演示了如何使用执行各个 Amazon Textract 操作。 AWS SDKs每个示例都包含一个指向的链接 GitHub，您可以在其中找到有关设置和运行代码的说明。 这些代码节选调用了 Amazon Textract API，是必须在上下文中运行的较大型程序的代码节选。您可以在[亚马逊 Textract 使用场景 AWS SDKs](textract_code_examples_scenarios.md)中结合上下文查看操作。 以下示例仅包括最常用的操作。有关完整列表，请参阅《[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) # `AnalyzeDocument`与 AWS SDK 或 CLI 配合使用 以下代码示例演示如何使用 `AnalyzeDocument`。 ------ #### [ CLI ] **AWS CLI** **分析文档中的文本** 以下 `analyze-document` 示例演示如何分析文档中的文本。 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 ``` 输出： ``` { "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 } } ``` 有关更多信息，请参阅《Amazon Textract 开发人员指南》**中的“使用 Amazon Textract 分析文档文本” + 有关 API 的详细信息，请参阅*AWS CLI 命令参考[AnalyzeDocument](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/textract/analyze-document.html)*中的。 ------ #### [ Java ] **适用于 Java 的 SDK 2.x** 还有更多相关信息 GitHub。在 [AWS 代码示例存储库](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javav2/example_code/textract#code-examples)中查找完整示例，了解如何进行设置和运行。 ``` 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); } } } ``` + 有关 API 的详细信息，请参阅 *AWS SDK for Java 2.x API 参考[AnalyzeDocument](https://docs.aws.amazon.com/goto/SdkForJavaV2/textract-2018-06-27/AnalyzeDocument)*中的。 ------ #### [ Python ] **适用于 Python 的 SDK（Boto3）** 还有更多相关信息 GitHub。在 [AWS 代码示例存储库](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/textract#code-examples)中查找完整示例，了解如何进行设置和运行。 ``` 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 ``` + 有关 API 的详细信息，请参阅适用[AnalyzeDocument](https://docs.aws.amazon.com/goto/boto3/textract-2018-06-27/AnalyzeDocument)于 *Python 的AWS SDK (Boto3) API 参考*。 ------ #### [ SAP ABAP ] **适用于 SAP ABAP 的 SDK** 还有更多相关信息 GitHub。在 [AWS 代码示例存储库](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/tex#code-examples)中查找完整示例，了解如何进行设置和运行。 ``` "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. ``` + 有关 API 的详细信息，请参阅适用[AnalyzeDocument](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)于 S *AP 的AWS SDK ABAP API 参考*。 ------ # `DetectDocumentText`与 AWS SDK 或 CLI 配合使用 以下代码示例演示如何使用 `DetectDocumentText`。 ------ #### [ CLI ] **AWS CLI** **检测文档中的文本** 以下 `detect-document-text` 示例演示了如何检测文档中的文本。 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 ``` 输出： ``` { "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 } } ``` 有关更多信息，请参阅《Amazon Textract 开发人员指南》**中的“使用 Amazon Textract 检测文档文本” + 有关 API 的详细信息，请参阅*AWS CLI 命令参考[DetectDocumentText](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/textract/detect-document-text.html)*中的。 ------ #### [ Java ] **适用于 Java 的 SDK 2.x** 还有更多相关信息 GitHub。在 [AWS 代码示例存储库](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javav2/example_code/textract#code-examples)中查找完整示例，了解如何进行设置和运行。 从输入文档检测文本。 ``` 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); } } } ``` 从位于 Amazon S3 桶中的文档检测文本。 ``` 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); } } } ``` + 有关 API 的详细信息，请参阅 *AWS SDK for Java 2.x API 参考[DetectDocumentText](https://docs.aws.amazon.com/goto/SdkForJavaV2/textract-2018-06-27/DetectDocumentText)*中的。 ------ #### [ Python ] **适用于 Python 的 SDK（Boto3）** 还有更多相关信息 GitHub。在 [AWS 代码示例存储库](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/textract#code-examples)中查找完整示例，了解如何进行设置和运行。 ``` 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 ``` + 有关 API 的详细信息，请参阅适用[DetectDocumentText](https://docs.aws.amazon.com/goto/boto3/textract-2018-06-27/DetectDocumentText)于 *Python 的AWS SDK (Boto3) API 参考*。 ------ #### [ SAP ABAP ] **适用于 SAP ABAP 的 SDK** 还有更多相关信息 GitHub。在 [AWS 代码示例存储库](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/tex#code-examples)中查找完整示例，了解如何进行设置和运行。 ``` "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. ``` + 有关 API 的详细信息，请参阅适用[DetectDocumentText](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)于 S *AP 的AWS SDK ABAP API 参考*。 ------ # `GetDocumentAnalysis`与 AWS SDK 或 CLI 配合使用 以下代码示例演示如何使用 `GetDocumentAnalysis`。 操作示例是大型程序的代码摘录，必须在上下文中运行。在以下代码示例中，您可以查看此操作的上下文： + [开始使用文档分析](textract_example_textract_Scenario_GettingStarted_section.md) ------ #### [ CLI ] **AWS CLI** **获取对多页文档进行异步文本分析的结果** 以下 `get-document-analysis` 示例演示了如何获取对多页文档进行异步文本分析的结果。 ``` aws textract get-document-analysis \ --job-id df7cf32ebbd2a5de113535fcf4d921926a701b09b4e7d089f3aebadb41e0712b \ --max-results 1000 ``` 输出： ``` { "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" } ``` 有关更多信息，请参阅《Amazon Textract 开发人员指南》**中的“检测和分析多页文档中的文本” + 有关 API 的详细信息，请参阅*AWS CLI 命令参考[GetDocumentAnalysis](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/textract/get-document-analysis.html)*中的。 ------ #### [ Python ] **适用于 Python 的 SDK（Boto3）** 还有更多相关信息 GitHub。在 [AWS 代码示例存储库](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/textract#code-examples)中查找完整示例，了解如何进行设置和运行。 ``` 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 ``` + 有关 API 的详细信息，请参阅适用[GetDocumentAnalysis](https://docs.aws.amazon.com/goto/boto3/textract-2018-06-27/GetDocumentAnalysis)于 *Python 的AWS SDK (Boto3) API 参考*。 ------ #### [ SAP ABAP ] **适用于 SAP ABAP 的 SDK** 还有更多相关信息 GitHub。在 [AWS 代码示例存储库](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/tex#code-examples)中查找完整示例，了解如何进行设置和运行。 ``` "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. ``` + 有关 API 的详细信息，请参阅适用[GetDocumentAnalysis](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)于 S *AP 的AWS SDK ABAP API 参考*。 ------ # `StartDocumentAnalysis`与 AWS SDK 或 CLI 配合使用 以下代码示例演示如何使用 `StartDocumentAnalysis`。 操作示例是大型程序的代码摘录，必须在上下文中运行。在以下代码示例中，您可以查看此操作的上下文： + [开始使用文档分析](textract_example_textract_Scenario_GettingStarted_section.md) ------ #### [ CLI ] **AWS CLI** **开始分析多页文档中的文本** 以下 `start-document-analysis` 示例演示如何开始异步分析多页文档中的文本。 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" ``` 输出： ``` { "JobId": "df7cf32ebbd2a5de113535fcf4d921926a701b09b4e7d089f3aebadb41e0712b" } ``` 有关更多信息，请参阅《Amazon Textract 开发人员指南》**中的“检测和分析多页文档中的文本” + 有关 API 的详细信息，请参阅*AWS CLI 命令参考[StartDocumentAnalysis](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/textract/start-document-analysis.html)*中的。 ------ #### [ Java ] **适用于 Java 的 SDK 2.x** 还有更多相关信息 GitHub。在 [AWS 代码示例存储库](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javav2/example_code/textract#code-examples)中查找完整示例，了解如何进行设置和运行。 ``` 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 ""; } } ``` + 有关 API 的详细信息，请参阅 *AWS SDK for Java 2.x API 参考[StartDocumentAnalysis](https://docs.aws.amazon.com/goto/SdkForJavaV2/textract-2018-06-27/StartDocumentAnalysis)*中的。 ------ #### [ Python ] **适用于 Python 的 SDK（Boto3）** 还有更多相关信息 GitHub。在 [AWS 代码示例存储库](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/textract#code-examples)中查找完整示例，了解如何进行设置和运行。 启动异步任务以分析文档。 ``` 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 ``` + 有关 API 的详细信息，请参阅适用[StartDocumentAnalysis](https://docs.aws.amazon.com/goto/boto3/textract-2018-06-27/StartDocumentAnalysis)于 *Python 的AWS SDK (Boto3) API 参考*。 ------ #### [ SAP ABAP ] **适用于 SAP ABAP 的 SDK** 还有更多相关信息 GitHub。在 [AWS 代码示例存储库](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/tex#code-examples)中查找完整示例，了解如何进行设置和运行。 ``` "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. ``` + 有关 API 的详细信息，请参阅适用[StartDocumentAnalysis](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)于 S *AP 的AWS SDK ABAP API 参考*。 ------ # `StartDocumentTextDetection`与 AWS SDK 或 CLI 配合使用 以下代码示例演示如何使用 `StartDocumentTextDetection`。 ------ #### [ CLI ] **AWS CLI** **开始检测多页文档中的文本** 以下 `start-document-text-detection` 示例演示如何开始异步检测多页文档中的文本。 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" ``` 输出： ``` { "JobId": "57849a3dc627d4df74123dca269d69f7b89329c870c65bb16c9fd63409d200b9" } ``` 有关更多信息，请参阅《Amazon Textract 开发人员指南》**中的“检测和分析多页文档中的文本” + 有关 API 的详细信息，请参阅*AWS CLI 命令参考[StartDocumentTextDetection](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/textract/start-document-text-detection.html)*中的。 ------ #### [ Python ] **适用于 Python 的 SDK（Boto3）** 还有更多相关信息 GitHub。在 [AWS 代码示例存储库](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/textract#code-examples)中查找完整示例，了解如何进行设置和运行。 启动异步任务以检测文档中的文本。 ``` 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 ``` + 有关 API 的详细信息，请参阅适用[StartDocumentTextDetection](https://docs.aws.amazon.com/goto/boto3/textract-2018-06-27/StartDocumentTextDetection)于 *Python 的AWS SDK (Boto3) API 参考*。 ------ #### [ SAP ABAP ] **适用于 SAP ABAP 的 SDK** 还有更多相关信息 GitHub。在 [AWS 代码示例存储库](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/tex#code-examples)中查找完整示例，了解如何进行设置和运行。 ``` "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. ``` + 有关 API 的详细信息，请参阅适用[StartDocumentTextDetection](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)于 S *AP 的AWS SDK ABAP API 参考*。 ------ # 亚马逊 Textract 使用场景 AWS SDKs 以下代码示例向您展示了如何使用在 Amazon Textract 中实现常见场景。 AWS SDKs这些场景演示了如何通过调用 Amazon Textract 中的多个函数或与其他 AWS 服务结合来完成特定任务。每个场景都包含完整源代码的链接，您可以在其中找到有关如何设置和运行代码的说明。 场景以中等水平的经验为目标，可帮助您结合具体环境了解服务操作。 **Topics** + [创建 Amazon Textract 浏览器应用程序](textract_example_cross_TextractExplorer_section.md) + [创建用于分析客户反馈的应用程序](textract_example_cross_FSA_section.md) + [检测从图像中提取的文本中的实体](textract_example_cross_TextractComprehendDetectEntities_section.md) + [开始使用文档分析](textract_example_textract_Scenario_GettingStarted_section.md) # 创建 Amazon Textract 浏览器应用程序 以下代码示例展示如何通过交互式应用程序探索 Amazon Textract 输出。 ------ #### [ JavaScript ] **适用于 JavaScript (v3) 的软件开发工具包** 演示如何使用 适用于 JavaScript 的 AWS SDK 来构建 React 应用程序，该应用程序使用 Amazon Textract 从文档图像中提取数据并将其显示在交互式网页中。此示例在 Web 浏览器中运行，需要经过身份验证的 Amazon Cognito 身份才能获得凭证。它使用 Amazon Simple Storage Service（Amazon S3）进行存储；对于通知，它将轮询订阅 Amazon Simple Notification Service（Amazon SNS）主题的 Amazon Simple Queue Service（Amazon SQS）队列。 有关如何设置和运行的完整源代码和说明，请参阅上的完整示例[GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javascriptv3/example_code/cross-services/textract-react)。 **本示例中使用的服务** + Amazon Cognito Identity + Amazon S3 + Amazon SNS + Amazon SQS + Amazon Textract ------ #### [ Python ] **适用于 Python 的 SDK（Boto3）** 演示如何 适用于 Python (Boto3) 的 AWS SDK 与 Amazon Textract 配合使用来检测文档图像中的文本、表单和表格元素。输入图像和 Amazon Textract 输出在 Tkinter 应用程序中显示，该应用程序可让您探索检测到的元素。 + 将文档图像提交到 Amazon Textract 并探索检测到的元素的输出。 + 将图像直接提交到 Amazon Textract，或通过 Amazon Simple Storage Service（Amazon S3）桶提交图像。 + 使用异步 APIs 启动任务，该任务在任务完成时向亚马逊简单通知服务 (Amazon SNS) Simple Notification Service 主题发布通知。 + 轮询 Amazon Simple Queue Service (Amazon SQS) 队列，以获取任务完成消息并显示结果。 有关如何设置和运行的完整源代码和说明，请参阅上的完整示例[GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/cross_service/textract_explorer)。 **本示例中使用的服务** + Amazon Cognito Identity + Amazon S3 + Amazon SNS + Amazon SQS + Amazon Textract ------ # 创建用于分析客户反馈和合成音频的应用程序 以下代码示例显示如何创建应用程序来分析客户意见卡、翻译其母语、确定其情绪并根据译后的文本生成音频文件。 ------ #### [ .NET ] **适用于 .NET 的 SDK** 此示例应用程序可分析并存储客户反馈卡。具体来说，它满足了纽约市一家虚构酒店的需求。酒店以实体意见卡的形式收集来自不同语种的客人的反馈。该反馈通过 Web 客户端上传到应用程序中。意见卡图片上传后，将执行以下步骤： + 使用 Amazon Textract 从图片中提取文本。 + Amazon Comprehend 确定所提取文本的情绪及其语言。 + 使用 Amazon Translate 将所提取文本翻译为英语。 + Amazon Polly 根据所提取文本合成音频文件。 完整的应用程序可使用  AWS CDK 进行部署。有关源代码和部署说明，请参阅中的项目[ GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/dotnetv3/cross-service/FeedbackSentimentAnalyzer)。 **本示例中使用的服务** + Amazon Comprehend + Lambda + Amazon Polly + Amazon Textract + Amazon Translate ------ #### [ Java ] **适用于 Java 的 SDK 2.x** 此示例应用程序可分析并存储客户反馈卡。具体来说，它满足了纽约市一家虚构酒店的需求。酒店以实体意见卡的形式收集来自不同语种的客人的反馈。该反馈通过 Web 客户端上传到应用程序中。意见卡图片上传后，将执行以下步骤： + 使用 Amazon Textract 从图片中提取文本。 + Amazon Comprehend 确定所提取文本的情绪及其语言。 + 使用 Amazon Translate 将所提取文本翻译为英语。 + Amazon Polly 根据所提取文本合成音频文件。 完整的应用程序可使用  AWS CDK 进行部署。有关源代码和部署说明，请参阅中的项目[ GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javav2/usecases/creating_fsa_app)。 **本示例中使用的服务** + Amazon Comprehend + Lambda + Amazon Polly + Amazon Textract + Amazon Translate ------ #### [ JavaScript ] **适用于 JavaScript (v3) 的软件开发工具包** 此示例应用程序可分析并存储客户反馈卡。具体来说，它满足了纽约市一家虚构酒店的需求。酒店以实体意见卡的形式收集来自不同语种的客人的反馈。该反馈通过 Web 客户端上传到应用程序中。意见卡图片上传后，将执行以下步骤： + 使用 Amazon Textract 从图片中提取文本。 + Amazon Comprehend 确定所提取文本的情绪及其语言。 + 使用 Amazon Translate 将所提取文本翻译为英语。 + Amazon Polly 根据所提取文本合成音频文件。 完整的应用程序可使用  AWS CDK 进行部署。有关源代码和部署说明，请参阅中的项目[ GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javascriptv3/example_code/cross-services/feedback-sentiment-analyzer)。以下摘录显示了在 Lambda 函数中 适用于 JavaScript 的 AWS SDK 是如何使用的。 ``` 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 }; }; ``` **本示例中使用的服务** + Amazon Comprehend + Lambda + Amazon Polly + Amazon Textract + Amazon Translate ------ #### [ Ruby ] **适用于 Ruby 的 SDK** 此示例应用程序可分析并存储客户反馈卡。具体来说，它满足了纽约市一家虚构酒店的需求。酒店以实体意见卡的形式收集来自不同语种的客人的反馈。该反馈通过 Web 客户端上传到应用程序中。意见卡图片上传后，将执行以下步骤： + 使用 Amazon Textract 从图片中提取文本。 + Amazon Comprehend 确定所提取文本的情绪及其语言。 + 使用 Amazon Translate 将所提取文本翻译为英语。 + Amazon Polly 根据所提取文本合成音频文件。 完整的应用程序可使用  AWS CDK 进行部署。有关源代码和部署说明，请参阅中的项目[ GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/ruby/cross_service_examples/feedback_sentiment_analyzer)。 **本示例中使用的服务** + Amazon Comprehend + Lambda + Amazon Polly + Amazon Textract + Amazon Translate ------ # 使用 AWS SDK 检测从图像中提取的文本中的实体 以下代码示例显示了如何使用 Amazon Comprehend 检测 Amazon Textract 从存储在 Amazon S3 内的图像中提取的文本中的实体。 ------ #### [ Python ] **适用于 Python 的 SDK（Boto3）** 演示如何使用 Jupyter 笔记本 适用于 Python (Boto3) 的 AWS SDK 中的来检测从图像中提取的文本中的实体。此示例使用 Amazon Textract 从存储在 Amazon Simple Storage Service (Amazon S3) 内的图像中提取文本，并使用 Amazon Comprehend 检测提取文本中的实体。 此示例是 Jupyter 笔记本，必须在可以托管笔记本电脑的环境中运行。有关如何使用 Amazon A SageMaker I 运行示例的说明，请参阅 [TextractAndComprehendNotebook.ipyn](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/cross_service/textract_comprehend_notebook/TextractAndComprehendNotebook.ipynb) b 中的说明。 有关如何设置和运行的完整源代码和说明，请参阅上的完整示例[GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/cross_service/textract_comprehend_notebook#readme)。 **本示例中使用的服务** + Amazon Comprehend + Amazon S3 + Amazon Textract ------ # 开始使用软件开发工具包进行 Amazon Textract 文档分析 AWS 以下代码示例展示了如何： + 开始异步分析。 + 获取文档分析。 ------ #### [ SAP ABAP ] **SDK for SAP ABAP** 还有更多相关信息 GitHub。在 [AWS 代码示例存储库](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/tex#code-examples)中查找完整示例，了解如何进行设置和运行。 ``` "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. ``` + 有关 API 详细信息，请参阅《AWS SDK for SAP ABAP API Reference》**中的以下主题。 + [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) ------