检测或分析多页文档中的文本

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。

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。

2. 为队列订阅主题。

3. 为向队列发送消息的主题授予权限。

4. 开始处理文档。对您选择的分析类型使用适当的操作：

   • StartDocumentTextDetection用于文本检测任务。

   • StartDocumentAnalysis用于文本分析任务。

   • StartExpenseAnalysis用于支出分析任务。

5. 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Startoperation. 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。

6. 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用于文本检测任务。

   • GetDocumentAnalysis用于文本分析任务。

   • GetExpenseAnalysis用于支出分析任务。

7. 删除 Amazon SNS 主题和 Amazon SQS 队列。

执行异步操作

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。

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。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。

2. 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中的Amazon Simple Storage Service 用户指南.

3. 使用以下命令AWS SDK for Java、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.

   • 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，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替换job-id-number使用job-id返回方StartDocumentTextDetection. 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\）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. 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()

4. 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。