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。

# 检测或分析多页文档中的文本
<a name="async-analyzing-with-sqs"></a>

此过程向您展示如何使用 Amazon Textract 检测操作、存储在 Amazon S3 存储桶中的文档、Amazon SNS 主题和 Amazon SQS 队列来检测或分析多页文档中的文本。多页文档处理是一项异步操作。有关更多信息，请参阅 [调用 Amazon Textract 异步操作](api-async.md)。

您可以选择希望代码执行的处理类型：文本检测、文本分析或费用分析。

处理结果将在数组中返回[Block](API_Block.md)对象，具体取决于您使用的处理类型。

 要检测多页文档中的文本或分析多页文档，请执行以下操作：

1. 创建 Amazon SNS 主题和 Amazon SQS 队列。

1. 为队列订阅主题。

1. 为向队列发送消息的主题授予权限。

1. 开始处理文档。对您选择的分析类型使用适当的操作：
   + [StartDocumentTextDetection](API_StartDocumentTextDetection.md)用于文本检测任务。
   + [StartDocumentAnalysis](API_StartDocumentAnalysis.md)用于文本分析任务。
   + [StartExpenseAnalysis](API_StartExpenseAnalysis.md)用于支出分析任务。

1. 从 Amazon SQS 队列获取完成状态。示例代码跟踪作业标识符（`JobId`) 这是由`Start`operation. 它仅获取与从完成状态读取的任务标识符匹配的结果。如果其他应用程序使用的是同一队列和主题，这一点很重要。为简便起见，该示例会删除不匹配的任务。请考虑将删除的任务添加到 Amazon SQS 死信队列以进行进一步调查。

1. 通过为所选分析类型调用适当的操作来获取并显示处理结果：
   + [GetDocumentTextDetection](API_GetDocumentTextDetection.md)用于文本检测任务。
   + [GetDocumentAnalysis](API_GetDocumentAnalysis.md)用于文本分析任务。
   + [GetExpenseAnalysis](API_GetExpenseAnalysis.md)用于支出分析任务。

1. 删除 Amazon SNS 主题和 Amazon SQS 队列。

## 执行异步操作
<a name="async-prerequisites"></a>

此过程的示例代码提供在 Java、Python 和AWS CLI. 在开始之前，请安装适当的AWSSDK。有关更多信息，请参阅 [第 2 步：设置AWS CLI和AWS软件开发工具包](setup-awscli-sdk.md)。

**检测或分析多页文档中的文本**

1. 配置用户访问 Amazon Textract 并配置 Amazon Textract 访问 Amazon SNS 的权限。有关更多信息，请参阅 [为异步操作配置 Amazon Textract](api-async-roles.md)。要完成此过程，您需要一个 PDF 格式的多页文档文件。请跳过步骤 3 — 6，因为示例代码将创建并配置 Amazon SNS 主题和 Amazon SQS 队列。如果很复杂t在 CLI 示例中，您无需设置 SQS 队列。

1. 将 PDF 或 TIFF 格式的多页文档文件上传到 Amazon S3 存储桶。（也可以处理 JPEG、PNG、TIFF 或 PDF 格式的单页文档）。

   有关说明，请参阅[将对象上传到 Amazon S3](https://docs.aws.amazon.com/AmazonS3/latest/user-guide/UploadingObjectsintoAmazonS3.html)中的*Amazon Simple Storage Service 用户指南*.

1. 使用以下命令适用于 Java 的 AWS SDK、SDK for Python (Boto3)，或AWS CLI用于检测文本或分析多页文档中的文本的代码。在`main`函数：
   + 替换的值`roleArn`使用您保存的 IAM 角色 ARN[授予 Amazon Textract 访问您的 Amazon SNS 主题](api-async-roles.md#api-async-roles-all-topics).
   + 替换的值`bucket`和`document`将包含您在步骤 2 中指定的存储桶和文档文件名。
   + 替换的值`type`的输入参数`ProcessDocument`函数与您要执行的处理类型。使用`ProcessType.DETECTION`来检测文本。使用`ProcessType.ANALYSIS`来分析文本。
   + 对于 Python 示例，请替换`region_name`您的客户运营所在的地区。

   对于AWS CLI例如，执行以下操作：
   + 打电话时[StartDocumentTextDetection](API_StartDocumentTextDetection.md)，替换的值`bucket-name`使用 S3 存储桶的名称，然后替换`file-name`将与您在步骤 2 中指定的文件名称。通过替换来指定存储桶的区域`region-name`用你所在地区的名称。请注意，CLI 示例没有使用 SQS。
   + 打电话时[GetDocumentTextDetection](API_GetDocumentTextDetection.md)替换`job-id-number`使用`job-id`返回方[StartDocumentTextDetection](API_StartDocumentTextDetection.md). 通过替换来指定存储桶的区域`region-name`用你所在地区的名称。

------
#### [ Java ]

   ```
   package com.amazonaws.samples;
   
   import java.util.Arrays;
   import java.util.HashMap;
   import java.util.List;
   import java.util.Map;
   
   import com.amazonaws.auth.policy.Condition;
   import com.amazonaws.auth.policy.Policy;
   import com.amazonaws.auth.policy.Principal;
   import com.amazonaws.auth.policy.Resource;
   import com.amazonaws.auth.policy.Statement;
   import com.amazonaws.auth.policy.Statement.Effect;
   import com.amazonaws.auth.policy.actions.SQSActions;
   import com.amazonaws.services.sns.AmazonSNS;
   import com.amazonaws.services.sns.AmazonSNSClientBuilder;
   import com.amazonaws.services.sns.model.CreateTopicRequest;
   import com.amazonaws.services.sns.model.CreateTopicResult;
   import com.amazonaws.services.sqs.AmazonSQS;
   import com.amazonaws.services.sqs.AmazonSQSClientBuilder;
   import com.amazonaws.services.sqs.model.CreateQueueRequest;
   import com.amazonaws.services.sqs.model.Message;
   import com.amazonaws.services.sqs.model.QueueAttributeName;
   import com.amazonaws.services.sqs.model.SetQueueAttributesRequest;
   import com.amazonaws.services.textract.AmazonTextract;
   import com.amazonaws.services.textract.AmazonTextractClientBuilder;
   import com.amazonaws.services.textract.model.Block;
   import com.amazonaws.services.textract.model.DocumentLocation;
   import com.amazonaws.services.textract.model.DocumentMetadata;
   import com.amazonaws.services.textract.model.GetDocumentAnalysisRequest;
   import com.amazonaws.services.textract.model.GetDocumentAnalysisResult;
   import com.amazonaws.services.textract.model.GetDocumentTextDetectionRequest;
   import com.amazonaws.services.textract.model.GetDocumentTextDetectionResult;
   import com.amazonaws.services.textract.model.NotificationChannel;
   import com.amazonaws.services.textract.model.Relationship;
   import com.amazonaws.services.textract.model.S3Object;
   import com.amazonaws.services.textract.model.StartDocumentAnalysisRequest;
   import com.amazonaws.services.textract.model.StartDocumentAnalysisResult;
   import com.amazonaws.services.textract.model.StartDocumentTextDetectionRequest;
   import com.amazonaws.services.textract.model.StartDocumentTextDetectionResult;
   import com.fasterxml.jackson.databind.JsonNode;
   import com.fasterxml.jackson.databind.ObjectMapper;;
   public class DocumentProcessor {
   
       private static String sqsQueueName=null;
       private static String snsTopicName=null;
       private static String snsTopicArn = null;
       private static String roleArn= null;
       private static String sqsQueueUrl = null;
       private static String sqsQueueArn = null;
       private static String startJobId = null;
       private static String bucket = null;
       private static String document = null; 
       private static AmazonSQS sqs=null;
       private static AmazonSNS sns=null;
       private static AmazonTextract textract = null;
   
       public enum ProcessType {
           DETECTION,ANALYSIS
       }
   
       public static void main(String[] args) throws Exception {
           
           String document = "document";
           String bucket = "bucket";
           String roleArn="role";
   
           sns = AmazonSNSClientBuilder.defaultClient();
           sqs= AmazonSQSClientBuilder.defaultClient();
           textract=AmazonTextractClientBuilder.defaultClient();
           
           CreateTopicandQueue();
           ProcessDocument(bucket,document,roleArn,ProcessType.DETECTION);
           DeleteTopicandQueue();
           System.out.println("Done!");
           
           
       }
       // Creates an SNS topic and SQS queue. The queue is subscribed to the topic. 
       static void CreateTopicandQueue()
       {
           //create a new SNS topic
           snsTopicName="AmazonTextractTopic" + Long.toString(System.currentTimeMillis());
           CreateTopicRequest createTopicRequest = new CreateTopicRequest(snsTopicName);
           CreateTopicResult createTopicResult = sns.createTopic(createTopicRequest);
           snsTopicArn=createTopicResult.getTopicArn();
           
           //Create a new SQS Queue
           sqsQueueName="AmazonTextractQueue" + Long.toString(System.currentTimeMillis());
           final CreateQueueRequest createQueueRequest = new CreateQueueRequest(sqsQueueName);
           sqsQueueUrl = sqs.createQueue(createQueueRequest).getQueueUrl();
           sqsQueueArn = sqs.getQueueAttributes(sqsQueueUrl, Arrays.asList("QueueArn")).getAttributes().get("QueueArn");
           
           //Subscribe SQS queue to SNS topic
           String sqsSubscriptionArn = sns.subscribe(snsTopicArn, "sqs", sqsQueueArn).getSubscriptionArn();
           
           // Authorize queue
             Policy policy = new Policy().withStatements(
                     new Statement(Effect.Allow)
                     .withPrincipals(Principal.AllUsers)
                     .withActions(SQSActions.SendMessage)
                     .withResources(new Resource(sqsQueueArn))
                     .withConditions(new Condition().withType("ArnEquals").withConditionKey("aws:SourceArn").withValues(snsTopicArn))
                     );
                     
   
             Map queueAttributes = new HashMap();
             queueAttributes.put(QueueAttributeName.Policy.toString(), policy.toJson());
             sqs.setQueueAttributes(new SetQueueAttributesRequest(sqsQueueUrl, queueAttributes)); 
             
   
            System.out.println("Topic arn: " + snsTopicArn);
            System.out.println("Queue arn: " + sqsQueueArn);
            System.out.println("Queue url: " + sqsQueueUrl);
            System.out.println("Queue sub arn: " + sqsSubscriptionArn );
        }
       static void DeleteTopicandQueue()
       {
           if (sqs !=null) {
               sqs.deleteQueue(sqsQueueUrl);
               System.out.println("SQS queue deleted");
           }
           
           if (sns!=null) {
               sns.deleteTopic(snsTopicArn);
               System.out.println("SNS topic deleted");
           }
       }
       
       //Starts the processing of the input document.
       static void ProcessDocument(String inBucket, String inDocument, String inRoleArn, ProcessType type) throws Exception
       {
           bucket=inBucket;
           document=inDocument;
           roleArn=inRoleArn;
   
           switch(type)
           {
               case DETECTION:
                   StartDocumentTextDetection(bucket, document);
                   System.out.println("Processing type: Detection");
                   break;
               case ANALYSIS:
                   StartDocumentAnalysis(bucket,document);
                   System.out.println("Processing type: Analysis");
                   break;
               default:
                   System.out.println("Invalid processing type. Choose Detection or Analysis");
                   throw new Exception("Invalid processing type");
              
           }
   
           System.out.println("Waiting for job: " + startJobId);
           //Poll queue for messages
           List<Message> messages=null;
           int dotLine=0;
           boolean jobFound=false;
   
           //loop until the job status is published. Ignore other messages in queue.
           do{
               messages = sqs.receiveMessage(sqsQueueUrl).getMessages();
               if (dotLine++<40){
                   System.out.print(".");
               }else{
                   System.out.println();
                   dotLine=0;
               }
   
               if (!messages.isEmpty()) {
                   //Loop through messages received.
                   for (Message message: messages) {
                       String notification = message.getBody();
   
                       // Get status and job id from notification.
                       ObjectMapper mapper = new ObjectMapper();
                       JsonNode jsonMessageTree = mapper.readTree(notification);
                       JsonNode messageBodyText = jsonMessageTree.get("Message");
                       ObjectMapper operationResultMapper = new ObjectMapper();
                       JsonNode jsonResultTree = operationResultMapper.readTree(messageBodyText.textValue());
                       JsonNode operationJobId = jsonResultTree.get("JobId");
                       JsonNode operationStatus = jsonResultTree.get("Status");
                       System.out.println("Job found was " + operationJobId);
                       // Found job. Get the results and display.
                       if(operationJobId.asText().equals(startJobId)){
                           jobFound=true;
                           System.out.println("Job id: " + operationJobId );
                           System.out.println("Status : " + operationStatus.toString());
                           if (operationStatus.asText().equals("SUCCEEDED")){
                               switch(type)
                               {
                                   case DETECTION:
                                       GetDocumentTextDetectionResults();
                                       break;
                                   case ANALYSIS:
                                       GetDocumentAnalysisResults();
                                       break;
                                   default:
                                       System.out.println("Invalid processing type. Choose Detection or Analysis");
                                       throw new Exception("Invalid processing type");
                                  
                               }
                           }
                           else{
                               System.out.println("Document analysis failed");
                           }
   
                           sqs.deleteMessage(sqsQueueUrl,message.getReceiptHandle());
                       }
   
                       else{
                           System.out.println("Job received was not job " +  startJobId);
                           //Delete unknown message. Consider moving message to dead letter queue
                           sqs.deleteMessage(sqsQueueUrl,message.getReceiptHandle());
                       }
                   }
               }
               else {
                   Thread.sleep(5000);
               }
           } while (!jobFound);
   
           System.out.println("Finished processing document");
       }
       
       private static void StartDocumentTextDetection(String bucket, String document) throws Exception{
   
           //Create notification channel 
           NotificationChannel channel= new NotificationChannel()
                   .withSNSTopicArn(snsTopicArn)
                   .withRoleArn(roleArn);
   
           StartDocumentTextDetectionRequest req = new StartDocumentTextDetectionRequest()
                   .withDocumentLocation(new DocumentLocation()
                       .withS3Object(new S3Object()
                           .withBucket(bucket)
                           .withName(document)))
                   .withJobTag("DetectingText")
                   .withNotificationChannel(channel);
   
           StartDocumentTextDetectionResult startDocumentTextDetectionResult = textract.startDocumentTextDetection(req);
           startJobId=startDocumentTextDetectionResult.getJobId();
       }
       
     //Gets the results of processing started by StartDocumentTextDetection
       private static void GetDocumentTextDetectionResults() throws Exception{
           int maxResults=1000;
           String paginationToken=null;
           GetDocumentTextDetectionResult response=null;
           Boolean finished=false;
           
           while (finished==false)
           {
               GetDocumentTextDetectionRequest documentTextDetectionRequest= new GetDocumentTextDetectionRequest()
                       .withJobId(startJobId)
                       .withMaxResults(maxResults)
                       .withNextToken(paginationToken);
               response = textract.getDocumentTextDetection(documentTextDetectionRequest);
               DocumentMetadata documentMetaData=response.getDocumentMetadata();
   
               System.out.println("Pages: " + documentMetaData.getPages().toString());
               
               //Show blocks information
               List<Block> blocks= response.getBlocks();
               for (Block block : blocks) {
                   DisplayBlockInfo(block);
               }
               paginationToken=response.getNextToken();
               if (paginationToken==null)
                   finished=true;
               
           }
           
       }
   
       private static void StartDocumentAnalysis(String bucket, String document) throws Exception{
           //Create notification channel 
           NotificationChannel channel= new NotificationChannel()
                   .withSNSTopicArn(snsTopicArn)
                   .withRoleArn(roleArn);
           
           StartDocumentAnalysisRequest req = new StartDocumentAnalysisRequest()
                   .withFeatureTypes("TABLES","FORMS")
                   .withDocumentLocation(new DocumentLocation()
                       .withS3Object(new S3Object()
                           .withBucket(bucket)
                           .withName(document)))
                   .withJobTag("AnalyzingText")
                   .withNotificationChannel(channel);
   
           StartDocumentAnalysisResult startDocumentAnalysisResult = textract.startDocumentAnalysis(req);
           startJobId=startDocumentAnalysisResult.getJobId();
       }
       //Gets the results of processing started by StartDocumentAnalysis
       private static void GetDocumentAnalysisResults() throws Exception{
   
           int maxResults=1000;
           String paginationToken=null;
           GetDocumentAnalysisResult response=null;
           Boolean finished=false;
           
           //loops until pagination token is null
           while (finished==false)
           {
               GetDocumentAnalysisRequest documentAnalysisRequest= new GetDocumentAnalysisRequest()
                       .withJobId(startJobId)
                       .withMaxResults(maxResults)
                       .withNextToken(paginationToken);
               
               response = textract.getDocumentAnalysis(documentAnalysisRequest);
   
               DocumentMetadata documentMetaData=response.getDocumentMetadata();
   
               System.out.println("Pages: " + documentMetaData.getPages().toString());
   
               //Show blocks, confidence and detection times
               List<Block> blocks= response.getBlocks();
   
               for (Block block : blocks) {
                   DisplayBlockInfo(block);
               }
               paginationToken=response.getNextToken();
               if (paginationToken==null)
                   finished=true;
           }
   
       }
       //Displays Block information for text detection and text analysis
       private static void DisplayBlockInfo(Block block) {
           System.out.println("Block Id : " + block.getId());
           if (block.getText()!=null)
               System.out.println("\tDetected text: " + block.getText());
           System.out.println("\tType: " + block.getBlockType());
           
           if (block.getBlockType().equals("PAGE") !=true) {
               System.out.println("\tConfidence: " + block.getConfidence().toString());
           }
           if(block.getBlockType().equals("CELL"))
           {
               System.out.println("\tCell information:");
               System.out.println("\t\tColumn: " + block.getColumnIndex());
               System.out.println("\t\tRow: " + block.getRowIndex());
               System.out.println("\t\tColumn span: " + block.getColumnSpan());
               System.out.println("\t\tRow span: " + block.getRowSpan());
   
           }
           
           System.out.println("\tRelationships");
           List<Relationship> relationships=block.getRelationships();
           if(relationships!=null) {
               for (Relationship relationship : relationships) {
                   System.out.println("\t\tType: " + relationship.getType());
                   System.out.println("\t\tIDs: " + relationship.getIds().toString());
               }
           } else {
               System.out.println("\t\tNo related Blocks");
           }
   
           System.out.println("\tGeometry");
           System.out.println("\t\tBounding Box: " + block.getGeometry().getBoundingBox().toString());
           System.out.println("\t\tPolygon: " + block.getGeometry().getPolygon().toString());
           
           List<String> entityTypes = block.getEntityTypes();
           
           System.out.println("\tEntity Types");
           if(entityTypes!=null) {
               for (String entityType : entityTypes) {
                   System.out.println("\t\tEntity Type: " + entityType);
               }
           } else {
               System.out.println("\t\tNo entity type");
           }
           
           if(block.getBlockType().equals("SELECTION_ELEMENT")) {
               System.out.print("    Selection element detected: ");
               if (block.getSelectionStatus().equals("SELECTED")){
                   System.out.println("Selected");
               }else {
                   System.out.println(" Not selected");
               }
           }
           if(block.getPage()!=null)
               System.out.println("\tPage: " + block.getPage());            
           System.out.println();
       }
   }
   ```

------
#### [ AWS CLI ]

   该AWS CLI命令启动异步检测指定文档中的文本。它返回`job-id`这可以用来检索检测结果。

   ```
   aws textract start-document-text-detection --document-location 
   "{\"S3Object\":{\"Bucket\":\"bucket-name\",\"Name\":\"file-name\"}}" --region region-name
   ```

   该AWS CLI命令返回 Amazon Textract 异步操作的结果（如果提供了`job-id`.

   ```
   aws textract get-document-text-detection --region region-name --job-id job-id-number
   ```

   如果您在 Windows 设备上访问 CLI，请使用双引号而不是单引号，并用反斜杠（即\$1）转义内部双引号，以解决可能遇到的任何解析器错误。有关示例，请参阅下文

   ```
   aws textract start-document-text-detection --document-location "{\"S3Object\":{\"Bucket\":\"bucket\",\"Name\":\"document\"}}" --region region-name
   ```

------
#### [ Python ]

   ```
   import boto3
   import json
   import sys
   import time
   
   
   class ProcessType:
       DETECTION = 1
       ANALYSIS = 2
   
   
   class DocumentProcessor:
       jobId = ''
       region_name = ''
   
       roleArn = ''
       bucket = ''
       document = ''
   
       sqsQueueUrl = ''
       snsTopicArn = ''
       processType = ''
   
       def __init__(self, role, bucket, document, region):
           self.roleArn = role
           self.bucket = bucket
           self.document = document
           self.region_name = region
   
           self.textract = boto3.client('textract', region_name=self.region_name)
           self.sqs = boto3.client('sqs')
           self.sns = boto3.client('sns')
   
       def ProcessDocument(self, type):
           jobFound = False
   
           self.processType = type
           validType = False
   
           # Determine which type of processing to perform
           if self.processType == ProcessType.DETECTION:
               response = self.textract.start_document_text_detection(
                   DocumentLocation={'S3Object': {'Bucket': self.bucket, 'Name': self.document}},
                   NotificationChannel={'RoleArn': self.roleArn, 'SNSTopicArn': self.snsTopicArn})
               print('Processing type: Detection')
               validType = True
   
           if self.processType == ProcessType.ANALYSIS:
               response = self.textract.start_document_analysis(
                   DocumentLocation={'S3Object': {'Bucket': self.bucket, 'Name': self.document}},
                   FeatureTypes=["TABLES", "FORMS"],
                   NotificationChannel={'RoleArn': self.roleArn, 'SNSTopicArn': self.snsTopicArn})
               print('Processing type: Analysis')
               validType = True
   
           if validType == False:
               print("Invalid processing type. Choose Detection or Analysis.")
               return
   
           print('Start Job Id: ' + response['JobId'])
           dotLine = 0
           while jobFound == False:
               sqsResponse = self.sqs.receive_message(QueueUrl=self.sqsQueueUrl, MessageAttributeNames=['ALL'],
                                                      MaxNumberOfMessages=10)
   
               if sqsResponse:
   
                   if 'Messages' not in sqsResponse:
                       if dotLine < 40:
                           print('.', end='')
                           dotLine = dotLine + 1
                       else:
                           print()
                           dotLine = 0
                       sys.stdout.flush()
                       time.sleep(5)
                       continue
   
                   for message in sqsResponse['Messages']:
                       notification = json.loads(message['Body'])
                       textMessage = json.loads(notification['Message'])
                       print(textMessage['JobId'])
                       print(textMessage['Status'])
                       if str(textMessage['JobId']) == response['JobId']:
                           print('Matching Job Found:' + textMessage['JobId'])
                           jobFound = True
                           self.GetResults(textMessage['JobId'])
                           self.sqs.delete_message(QueueUrl=self.sqsQueueUrl,
                                                   ReceiptHandle=message['ReceiptHandle'])
                       else:
                           print("Job didn't match:" +
                                 str(textMessage['JobId']) + ' : ' + str(response['JobId']))
                       # Delete the unknown message. Consider sending to dead letter queue
                       self.sqs.delete_message(QueueUrl=self.sqsQueueUrl,
                                               ReceiptHandle=message['ReceiptHandle'])
   
           print('Done!')
   
       def CreateTopicandQueue(self):
   
           millis = str(int(round(time.time() * 1000)))
   
           # Create SNS topic
           snsTopicName = "AmazonTextractTopic" + millis
   
           topicResponse = self.sns.create_topic(Name=snsTopicName)
           self.snsTopicArn = topicResponse['TopicArn']
   
           # create SQS queue
           sqsQueueName = "AmazonTextractQueue" + millis
           self.sqs.create_queue(QueueName=sqsQueueName)
           self.sqsQueueUrl = self.sqs.get_queue_url(QueueName=sqsQueueName)['QueueUrl']
   
           attribs = self.sqs.get_queue_attributes(QueueUrl=self.sqsQueueUrl,
                                                   AttributeNames=['QueueArn'])['Attributes']
   
           sqsQueueArn = attribs['QueueArn']
   
           # Subscribe SQS queue to SNS topic
           self.sns.subscribe(
               TopicArn=self.snsTopicArn,
               Protocol='sqs',
               Endpoint=sqsQueueArn)
   
           # Authorize SNS to write SQS queue
           policy = """{{
     "Version":"2012-10-17",
     "Statement":[
       {{
         "Sid":"MyPolicy",
         "Effect":"Allow",
         "Principal" : {{"AWS" : "*"}},
         "Action":"SQS:SendMessage",
         "Resource": "{}",
         "Condition":{{
           "ArnEquals":{{
             "aws:SourceArn": "{}"
           }}
         }}
       }}
     ]
   }}""".format(sqsQueueArn, self.snsTopicArn)
   
           response = self.sqs.set_queue_attributes(
               QueueUrl=self.sqsQueueUrl,
               Attributes={
                   'Policy': policy
               })
   
       def DeleteTopicandQueue(self):
           self.sqs.delete_queue(QueueUrl=self.sqsQueueUrl)
           self.sns.delete_topic(TopicArn=self.snsTopicArn)
   
       # Display information about a block
       def DisplayBlockInfo(self, block):
   
           print("Block Id: " + block['Id'])
           print("Type: " + block['BlockType'])
           if 'EntityTypes' in block:
               print('EntityTypes: {}'.format(block['EntityTypes']))
   
           if 'Text' in block:
               print("Text: " + block['Text'])
   
           if block['BlockType'] != 'PAGE':
               print("Confidence: " + "{:.2f}".format(block['Confidence']) + "%")
   
           print('Page: {}'.format(block['Page']))
   
           if block['BlockType'] == 'CELL':
               print('Cell Information')
               print('\tColumn: {} '.format(block['ColumnIndex']))
               print('\tRow: {}'.format(block['RowIndex']))
               print('\tColumn span: {} '.format(block['ColumnSpan']))
               print('\tRow span: {}'.format(block['RowSpan']))
   
               if 'Relationships' in block:
                   print('\tRelationships: {}'.format(block['Relationships']))
   
           print('Geometry')
           print('\tBounding Box: {}'.format(block['Geometry']['BoundingBox']))
           print('\tPolygon: {}'.format(block['Geometry']['Polygon']))
   
           if block['BlockType'] == 'SELECTION_ELEMENT':
               print('    Selection element detected: ', end='')
               if block['SelectionStatus'] == 'SELECTED':
                   print('Selected')
               else:
                   print('Not selected')
   
       def GetResults(self, jobId):
           maxResults = 1000
           paginationToken = None
           finished = False
   
           while finished == False:
   
               response = None
   
               if self.processType == ProcessType.ANALYSIS:
                   if paginationToken == None:
                       response = self.textract.get_document_analysis(JobId=jobId,
                                                                      MaxResults=maxResults)
                   else:
                       response = self.textract.get_document_analysis(JobId=jobId,
                                                                      MaxResults=maxResults,
                                                                      NextToken=paginationToken)
   
               if self.processType == ProcessType.DETECTION:
                   if paginationToken == None:
                       response = self.textract.get_document_text_detection(JobId=jobId,
                                                                            MaxResults=maxResults)
                   else:
                       response = self.textract.get_document_text_detection(JobId=jobId,
                                                                            MaxResults=maxResults,
                                                                            NextToken=paginationToken)
   
               blocks = response['Blocks']
               print('Detected Document Text')
               print('Pages: {}'.format(response['DocumentMetadata']['Pages']))
   
               # Display block information
               for block in blocks:
                   self.DisplayBlockInfo(block)
                   print()
                   print()
   
               if 'NextToken' in response:
                   paginationToken = response['NextToken']
               else:
                   finished = True
   
       def GetResultsDocumentAnalysis(self, jobId):
           maxResults = 1000
           paginationToken = None
           finished = False
   
           while finished == False:
   
               response = None
               if paginationToken == None:
                   response = self.textract.get_document_analysis(JobId=jobId,
                                                                  MaxResults=maxResults)
               else:
                   response = self.textract.get_document_analysis(JobId=jobId,
                                                                  MaxResults=maxResults,
                                                                  NextToken=paginationToken)
   
                   # Get the text blocks
               blocks = response['Blocks']
               print('Analyzed Document Text')
               print('Pages: {}'.format(response['DocumentMetadata']['Pages']))
               # Display block information
               for block in blocks:
                   self.DisplayBlockInfo(block)
                   print()
                   print()
   
                   if 'NextToken' in response:
                       paginationToken = response['NextToken']
                   else:
                       finished = True
   
   
   def main():
       roleArn = ''
       bucket = ''
       document = ''
       region_name = ''
   
       analyzer = DocumentProcessor(roleArn, bucket, document, region_name)
       analyzer.CreateTopicandQueue()
       analyzer.ProcessDocument(ProcessType.DETECTION)
       analyzer.DeleteTopicandQueue()
   
   
   if __name__ == "__main__":
       main()
   ```

------
#### [ Node.JS ]

   在本示例中，请替换`roleArn`使用您保存的 IAM 角色 ARN[授予 Amazon Textract 访问您的 Amazon SNS 主题](api-async-roles.md#api-async-roles-all-topics). 替换的值`bucket`和`document`将使用您在上面步骤 2 中指定的存储桶和文档文件名。替换的值`processType`使用你想在输入文档上使用的处理类型。最后，将值替换为`REGION`您的客户运营所在的地区。

   ```
    // snippet-start:[sqs.JavaScript.queues.createQueueV3]
   // Import required AWS SDK clients and commands for Node.js
   import { CreateQueueCommand, GetQueueAttributesCommand, GetQueueUrlCommand, 
       SetQueueAttributesCommand, DeleteQueueCommand, ReceiveMessageCommand, DeleteMessageCommand } from  "@aws-sdk/client-sqs";
     import {CreateTopicCommand, SubscribeCommand, DeleteTopicCommand } from "@aws-sdk/client-sns";
     import  { SQSClient } from "@aws-sdk/client-sqs";
     import  { SNSClient } from "@aws-sdk/client-sns";
     import  { TextractClient, StartDocumentTextDetectionCommand, StartDocumentAnalysisCommand, GetDocumentAnalysisCommand, GetDocumentTextDetectionCommand, DocumentMetadata } from "@aws-sdk/client-textract";
     import { stdout } from "process";
     
     // Set the AWS Region.
     const REGION = "us-east-1"; //e.g. "us-east-1"
     // Create SNS service object.
     const sqsClient = new SQSClient({ region: REGION });
     const snsClient = new SNSClient({ region: REGION });
     const textractClient = new TextractClient({ region: REGION });
     
     // Set bucket and video variables
     const bucket = "bucket-name";                                                                                                                  
     const documentName = "document-name";
     const roleArn = "role-arn"
     const processType = "DETECTION"
     var startJobId = ""
     
     var ts = Date.now();
     const snsTopicName = "AmazonTextractExample" + ts;
     const snsTopicParams = {Name: snsTopicName}
     const sqsQueueName = "AmazonTextractQueue-" + ts;
   
     // Set the parameters
     const sqsParams = {
       QueueName: sqsQueueName, //SQS_QUEUE_URL
       Attributes: {
         DelaySeconds: "60", // Number of seconds delay.
         MessageRetentionPeriod: "86400", // Number of seconds delay.
       },
     };
     
     // Process a document based on operation type
     const processDocumment = async (type, bucket, videoName, roleArn, sqsQueueUrl, snsTopicArn) =>
       {
       try
       {
           // Set job found and success status to false initially
         var jobFound = false
         var succeeded = false
         var dotLine = 0
         var processType = type
         var validType = false
   
         if (processType == "DETECTION"){
           var response = await textractClient.send(new StartDocumentTextDetectionCommand({DocumentLocation:{S3Object:{Bucket:bucket, Name:videoName}}, 
             NotificationChannel:{RoleArn: roleArn, SNSTopicArn: snsTopicArn}}))
           console.log("Processing type: Detection")
           validType = true
         }
   
         if (processType == "ANALYSIS"){
           var response = await textractClient.send(new StartDocumentAnalysisCommand({DocumentLocation:{S3Object:{Bucket:bucket, Name:videoName}}, 
             NotificationChannel:{RoleArn: roleArn, SNSTopicArn: snsTopicArn}}))
           console.log("Processing type: Analysis")
           validType = true
         }
   
         if (validType == false){
             console.log("Invalid processing type. Choose Detection or Analysis.")
             return
         }
       // while not found, continue to poll for response
       console.log(`Start Job ID: ${response.JobId}`)
       while (jobFound == false){
         var sqsReceivedResponse = await sqsClient.send(new ReceiveMessageCommand({QueueUrl:sqsQueueUrl, 
           MaxNumberOfMessages:'ALL', MaxNumberOfMessages:10}));
         if (sqsReceivedResponse){
           var responseString = JSON.stringify(sqsReceivedResponse)
           if (!responseString.includes('Body')){
             if (dotLine < 40) {
               console.log('.')
               dotLine = dotLine + 1
             }else {
               console.log('')
               dotLine = 0 
             };
             stdout.write('', () => {
               console.log('');
             });
             await new Promise(resolve => setTimeout(resolve, 5000));
             continue
           }
         }
   
           // Once job found, log Job ID and return true if status is succeeded
           for (var message of sqsReceivedResponse.Messages){
               console.log("Retrieved messages:")
               var notification = JSON.parse(message.Body)
               var rekMessage = JSON.parse(notification.Message)
               var messageJobId = rekMessage.JobId
               if (String(rekMessage.JobId).includes(String(startJobId))){
                   console.log('Matching job found:')
                   console.log(rekMessage.JobId)
                   jobFound = true
                   // GET RESUlTS FUNCTION HERE
                   var operationResults = await GetResults(processType, rekMessage.JobId)
                   //GET RESULTS FUMCTION HERE
                   console.log(rekMessage.Status)
               if (String(rekMessage.Status).includes(String("SUCCEEDED"))){
                   succeeded = true
                   console.log("Job processing succeeded.")
                   var sqsDeleteMessage = await sqsClient.send(new DeleteMessageCommand({QueueUrl:sqsQueueUrl, ReceiptHandle:message.ReceiptHandle}));
               }
               }else{
               console.log("Provided Job ID did not match returned ID.")
               var sqsDeleteMessage = await sqsClient.send(new DeleteMessageCommand({QueueUrl:sqsQueueUrl, ReceiptHandle:message.ReceiptHandle}));
               }
           }
   
       console.log("Done!")
       }
       }catch (err) {
           console.log("Error", err);
         }
     }
   
     // Create the SNS topic and SQS Queue
     const createTopicandQueue = async () => {
       try {
         // Create SNS topic
         const topicResponse = await snsClient.send(new CreateTopicCommand(snsTopicParams));
         const topicArn = topicResponse.TopicArn
         console.log("Success", topicResponse);
         // Create SQS Queue
         const sqsResponse = await sqsClient.send(new CreateQueueCommand(sqsParams));
         console.log("Success", sqsResponse);
         const sqsQueueCommand = await sqsClient.send(new GetQueueUrlCommand({QueueName: sqsQueueName}))
         const sqsQueueUrl = sqsQueueCommand.QueueUrl
         const attribsResponse = await sqsClient.send(new GetQueueAttributesCommand({QueueUrl: sqsQueueUrl, AttributeNames: ['QueueArn']}))
         const attribs = attribsResponse.Attributes
         console.log(attribs)
         const queueArn = attribs.QueueArn
         // subscribe SQS queue to SNS topic
         const subscribed = await snsClient.send(new SubscribeCommand({TopicArn: topicArn, Protocol:'sqs', Endpoint: queueArn}))
         const policy = {
           Version: "2012-10-17",
           Statement: [
             {
               Sid: "MyPolicy",
               Effect: "Allow",
               Principal: {AWS: "*"},
               Action: "SQS:SendMessage",
               Resource: queueArn,
               Condition: {
                 ArnEquals: {
                   'aws:SourceArn': topicArn
                 }
               }
             }
           ]
         };
     
         const response = sqsClient.send(new SetQueueAttributesCommand({QueueUrl: sqsQueueUrl, Attributes: {Policy: JSON.stringify(policy)}}))
         console.log(response)
         console.log(sqsQueueUrl, topicArn)
         return [sqsQueueUrl, topicArn]
     
       } catch (err) {
         console.log("Error", err);
   
       }
     }
   
     const deleteTopicAndQueue = async (sqsQueueUrlArg, snsTopicArnArg) => {
       const deleteQueue = await sqsClient.send(new DeleteQueueCommand({QueueUrl: sqsQueueUrlArg}));
       const deleteTopic = await snsClient.send(new DeleteTopicCommand({TopicArn: snsTopicArnArg}));
       console.log("Successfully deleted.")
     }
   
     const displayBlockInfo = async (block) => {
       console.log(`Block ID: ${block.Id}`)
       console.log(`Block Type: ${block.BlockType}`)
       if (String(block).includes(String("EntityTypes"))){
           console.log(`EntityTypes: ${block.EntityTypes}`)
       }
       if (String(block).includes(String("Text"))){
           console.log(`EntityTypes: ${block.Text}`)
       }
       if (!String(block.BlockType).includes('PAGE')){
           console.log(`Confidence: ${block.Confidence}`)
       }
       console.log(`Page: ${block.Page}`)
       if (String(block.BlockType).includes("CELL")){
           console.log("Cell Information")
           console.log(`Column: ${block.ColumnIndex}`)
           console.log(`Row: ${block.RowIndex}`)
           console.log(`Column Span: ${block.ColumnSpan}`)
           console.log(`Row Span: ${block.RowSpan}`)
           if (String(block).includes("Relationships")){
               console.log(`Relationships: ${block.Relationships}`)
           }
       }
   
       console.log("Geometry")
       console.log(`Bounding Box: ${JSON.stringify(block.Geometry.BoundingBox)}`)
       console.log(`Polygon: ${JSON.stringify(block.Geometry.Polygon)}`)
   
       if (String(block.BlockType).includes('SELECTION_ELEMENT')){
         console.log('Selection Element detected:')
         if (String(block.SelectionStatus).includes('SELECTED')){
           console.log('Selected')
         } else {
           console.log('Not Selected')
         }
   
       }
     }
   
     const GetResults = async (processType, JobID) => {
   
       var maxResults = 1000
       var paginationToken = null
       var finished = false
   
       while (finished == false){
         var response = null
         if (processType == 'ANALYSIS'){
           if (paginationToken == null){
             response = textractClient.send(new GetDocumentAnalysisCommand({JobId:JobID, MaxResults:maxResults}))
         
           }else{
             response = textractClient.send(new GetDocumentAnalysisCommand({JobId:JobID, MaxResults:maxResults, NextToken:paginationToken}))
           }
         }
           
         if(processType == 'DETECTION'){
           if (paginationToken == null){
             response = textractClient.send(new GetDocumentTextDetectionCommand({JobId:JobID, MaxResults:maxResults}))
         
           }else{
             response = textractClient.send(new GetDocumentTextDetectionCommand({JobId:JobID, MaxResults:maxResults, NextToken:paginationToken}))
           }
         }
   
         await new Promise(resolve => setTimeout(resolve, 5000));
         console.log("Detected Documented Text")
         console.log(response)
         //console.log(Object.keys(response))
         console.log(typeof(response))
         var blocks = (await response).Blocks
         console.log(blocks)
         console.log(typeof(blocks))
         var docMetadata = (await response).DocumentMetadata
         var blockString = JSON.stringify(blocks)
         var parsed = JSON.parse(JSON.stringify(blocks))
         console.log(Object.keys(blocks))
         console.log(`Pages: ${docMetadata.Pages}`)
         blocks.forEach((block)=> {
           displayBlockInfo(block)
           console.log()
           console.log()
         })
   
         //console.log(blocks[0].BlockType)
         //console.log(blocks[1].BlockType)
   
   
         if(String(response).includes("NextToken")){
           paginationToken = response.NextToken
         }else{
           finished = true
         }
       }
   
     }
   
   
     // DELETE TOPIC AND QUEUE
     const main = async () => {
       var sqsAndTopic = await createTopicandQueue();
       var process = await processDocumment(processType, bucket, documentName, roleArn, sqsAndTopic[0], sqsAndTopic[1])
       var deleteResults = await deleteTopicAndQueue(sqsAndTopic[0], sqsAndTopic[1])
     }
   
   main()
   ```

------

1. 运行该代码。此操作可能需要一段时间才能完成。完成后，将显示检测到或分析的文本的块列表。