翻訳は機械翻訳により提供されています。提供された翻訳内容と英語版の間で齟齬、不一致または矛盾がある場合、英語版が優先します。

# 複数ページドキュメント内のテキストの検出または分析
<a name="async-analyzing-with-sqs"></a>

この手順では、Amazon Textract 検出オペレーション、Amazon S3 バケットに格納されたドキュメント、Amazon SNS トピック、および Amazon SQS キューを使用して、複数ページのドキュメント内のテキストを検出または分析する方法について説明します。複数ページのドキュメント処理は、非同期オペレーションです。詳細については、「[Amazon Textract 非同期オペレーションを呼び出す](api-async.md)」を参照してください。

コードで実行する処理のタイプ (テキスト検出、テキスト分析、または経費分析) を選択できます。

処理結果は、次の配列で返されます。[Block](API_Block.md)オブジェクト。使用する処理の種類によって異なります。

 複数ページのドキュメント内のテキストを検出したり、分析したりするには、次の操作を行います。

1. Amazon SNS トピックと Amazon SQS キューを作成します。

1. トピックをキューにサブスクライブします。

1. キューにメッセージを送信するアクセス許可をトピックに付与します。

1. ドキュメントの処理を開始します。選択した解析タイプに適切な操作を使用します。
   + [StartDocumentTextDetection](API_StartDocumentTextDetection.md)テキスト検出タスク用。
   + [StartDocumentAnalysis](API_StartDocumentAnalysis.md)テキスト分析タスク用。
   + [StartExpenseAnalysis](API_StartExpenseAnalysis.md)経費分析タスクの場合。

1. 完了ステータスを Amazon SQS キューから取得します。サンプルコードは、ジョブ識別子 () を追跡します。`JobId`) によって返される`Start`オペレーション. 完了ステータスから読み取られた、一致するジョブ識別子の結果だけを取得します。これは、他のアプリケーションが同じキューとトピックを使用している場合、重要です。わかりやすいように、この例では一致しないジョブが削除されます。削除したジョブを Amazon SQS デッドレターキューに追加して詳しく調査することを検討してください。

1. 選択した解析タイプの適切なオペレーションを呼び出して、処理結果を取得して表示します。
   + [GetDocumentTextDetection](API_GetDocumentTextDetection.md)テキスト検出タスク用。
   + [GetDocumentAnalysis](API_GetDocumentAnalysis.md)テキスト分析タスク用。
   + [GetExpenseAnalysis](API_GetExpenseAnalysis.md)経費分析タスクの場合。

1. Amazon SNS トピックと Amazon SQS キューを削除します。

## 非同期操作の実行
<a name="async-prerequisites"></a>

この手順のコード例は Java、Python、AWS CLI。開始する前に、適切なをインストールします。AWSSDK。詳細については、「[ステップ 2: のセットアップAWS CLIそしてAWSSDK](setup-awscli-sdk.md)」を参照してください。

**複数ページのドキュメント内のテキストを検出または分析するには**

1. Amazon Textract へのユーザーアクセスを設定し、Amazon SNS への Amazon Textract アクセスを設定します。詳細については、「[非同期オペレーション用の Amazon Textract の設定](api-async-roles.md)」を参照してください。この手順を完了するには、PDF 形式の複数ページのドキュメントファイルが必要です。コード例によって Amazon SNS トピックと Amazon SQS キューが作成されて設定されるため、ステップ 3 ～ 6 をスキップします。もし複雑ならtCLI の例では、SQS キューを設定する必要はありません。

1. PDF または TIFF 形式の複数ページのドキュメントファイルを Amazon S3 バケットにアップロードします。（JPEG、PNG、TIFF、または PDF 形式の単一ページのドキュメントも処理できます）。

   手順については、以下を参照してください。[Amazon S3 へのオブジェクトのアップロード](https://docs.aws.amazon.com/AmazonS3/latest/user-guide/UploadingObjectsintoAmazonS3.html)の*Amazon Simple Storage Service ユーザーガイド*。

1. 以下を使用します。AWS SDK for Java、SDK for Python (Boto3)、またはAWS CLI複数ページの文書でテキストを検出したり、テキストを分析したりするためのコードです。左`main`関数:
   + の値を置換する`roleArn`で保存した IAM ロール ARN を持つ[Amazon SNS トピックへの Amazon Textract アクセスを付与する](api-async-roles.md#api-async-roles-all-topics)。
   + の値を置換する`bucket`そして`document`に、ステップ 2 で指定したバケット名とドキュメントファイル名を使用します。
   + の値を置換します。`type`の入力パラメータ`ProcessDocument`関数を使用して、実行する処理の種類を指定します。を使用する`ProcessType.DETECTION`テキストを検出します。を使用する`ProcessType.ANALYSIS`テキストを分析します。
   + Python の例では、の値を置き換えます。`region_name`クライアントが操作している地域で。

   向けのAWS CLIたとえば、以下を実行します。
   + 呼び出し時[StartDocumentTextDetection](API_StartDocumentTextDetection.md)で、の値を置き換えます。`bucket-name`を S3 バケットの名前に置き換えます。`file-name`に、ステップ 2 で指定したファイル名を使用します。置き換えてバケットのリージョンを指定します。`region-name`お住まいの地域の名前を付けて。CLI の例では SQS を使用しないことに注意してください。
   + 呼び出し時[GetDocumentTextDetection](API_GetDocumentTextDetection.md)置換`job-id-number`と`job-id`によって返される[StartDocumentTextDetection](API_StartDocumentTextDetection.md)。置き換えてバケットのリージョンを指定します。`region-name`お住まいの地域の名前を付けて。

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

   このAWS CLIコマンドは、指定したドキュメント内のテキストの非同期検出を開始します。このメソッドからは、`job-id`これにより、検出結果を取得できます。

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

   このAWS CLIコマンドを使用すると、Amazon Textract 非同期オペレーションの結果が返されます。`job-id`。

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

   Windows デバイスで CLI にアクセスする場合は、一重引用符ではなく二重引用符を使用し、内部の二重引用符をバックスラッシュ（つまり\$1）でエスケープして、発生する可能性のあるパーサーエラーに対処します。例については、以下を参照してください

   ```
   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 ]

   この例では、の値を置き換えます。`roleArn`で保存した IAM ロール ARN を持つ[Amazon SNS トピックへの Amazon Textract アクセスを付与する](api-async-roles.md#api-async-roles-all-topics)。の値を置換する`bucket`そして`document`は、上記のステップ 2 で指定したバケット名とドキュメントファイル名を使用します。の値を置換する`processType`入力ドキュメントで使用する処理の種類を指定します。最後に、の値を置き換えます。`REGION`クライアントが操作している地域で。

   ```
    // 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. コードを実行します。オペレーションの完了までに時間がかかる場合があります。完了すると、検出または分析されたテキストのブロックのリストが表示されます。