Détection ou analyse du texte dans un document de plusieurs pages

Pour détecter ou analyser du texte dans un document de plusieurs pages

1. Configurez l'accès utilisateur à Amazon Textract et configurez l'accès Amazon Textract à Amazon SNS. Pour de plus amples informations, veuillez consulter Configuration d'Amazon Textract pour les opérations asynchrones. Pour effectuer cette procédure, vous avez besoin d'un fichier de plusieurs pages au format PDF. Ignorez les étapes 3 à 6 car l'exemple de code crée et configure la rubrique Amazon SNS et la file d'attente Amazon SQS. Si vous suivez l'exemple de CLI, vous n'avez pas besoin de configurer une file d'attente SQS.

2. Téléchargez un document de plusieurs pages au format PDF ou TIFF dans votre compartiment Amazon S3. (Single-page les documents au format JPEG, PNG, TIFF ou PDF peuvent également être traités).

   Pour en savoir plus, consultez Chargement d’objets dans Amazon S3 dans le Guide de l’utilisateur Amazon Simple Storage Service.

3. Utilisez le SDK pour Python (Boto3) AWS CLI ou le code suivant AWS SDK for Java pour détecter du texte ou analyser du texte dans un document de plusieurs pages. Dans la main fonction :

   • Remplacez la valeur de roleArn par l'ARN du rôle IAM dans Donner à Amazon Textract l'accès à votre rubrique Amazon SNS lequel vous avez enregistré.

   • Remplacez les valeurs de bucket et document par le nom du bucket et du fichier de document que vous avez spécifiés à l'étape 2.

   • Remplacez la valeur du paramètre type d'entrée de la ProcessDocument fonction par le type de traitement que vous souhaitez effectuer. ProcessType.DETECTIONÀ utiliser pour détecter du texte. ProcessType.ANALYSISÀ utiliser pour analyser du texte.

   • Pour l'exemple Python, remplacez la valeur de region_name par la région dans laquelle votre client opère.

   Pour l' AWS CLI exemple, procédez comme suit :

   • Lors de l'appel StartDocumentTextDetection, remplacez la valeur de bucket-name par le nom de votre compartiment S3 et file-name par le nom du fichier que vous avez spécifié à l'étape 2. Spécifiez la région de votre compartiment en la region-name remplaçant par le nom de votre région. Notez que l'exemple de CLI n'utilise pas SQS.

   • Lorsque vous appelez, GetDocumentTextDetectionremplacez job-id-number par le job-id renvoyé par StartDocumentTextDetection. Spécifiez la région de votre compartiment en la region-name remplaçant par le nom de votre région.

   Java

   Remplacez la valeur de credentialsProvider par le nom de votre profil de développeur.

   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.auth.profile.ProfileCredentialsProvider;
   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";
           
           // set provider credentials
           AWSCredentialsProvider credentialsProvider = new ProfileCredentialsProvider("default");
   
           sns = AmazonSNSClientBuilder.withCredentials(credentialsProvider)
                   .withRegion(Regions.US_EAST_1)
                   .build();
           sqs= AmazonSQSClientBuilder.withCredentials(credentialsProvider)
                   .withRegion(Regions.US_EAST_1)
                   .build();
           textract=AmazonTextractClientBuilder.withCredentials(credentialsProvider)
                   .withRegion(Regions.US_EAST_1)
                   .build();
           
           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();
       }
   }

   Java V2

   Remplacez la valeur de profile-name dans la ligne qui crée le TextractClient par le nom de votre profil de développeur.

   import software.amazon.awssdk.auth.credentials.ProfileCredentialsProvider;
   import software.amazon.awssdk.regions.Region;
   import software.amazon.awssdk.services.textract.model.S3Object;
   import software.amazon.awssdk.services.textract.TextractClient;
   import software.amazon.awssdk.services.textract.model.StartDocumentAnalysisRequest;
   import software.amazon.awssdk.services.textract.model.DocumentLocation;
   import software.amazon.awssdk.services.textract.model.TextractException;
   import software.amazon.awssdk.services.textract.model.StartDocumentAnalysisResponse;
   import software.amazon.awssdk.services.textract.model.GetDocumentAnalysisRequest;
   import software.amazon.awssdk.services.textract.model.GetDocumentAnalysisResponse;
   import software.amazon.awssdk.services.textract.model.FeatureType;
   import java.util.ArrayList;
   import java.util.List;
   // snippet-end:[textract.java2._start_doc_analysis.import]
   
   /**
    * Before running this Java V2 code example, set up your development environment, including your credentials.
    *
    * For more information, see the following documentation topic:
    *
    * https://docs.aws.amazon.com/sdk-for-java/latest/developer-guide/get-started.html
    */
   public class StartDocumentAnalysis {
   
       public static void main(String[] args) {
   
           final String usage = "\n" +
               "Usage:\n" +
               "    <bucketName> <docName> \n\n" +
               "Where:\n" +
               "    bucketName - The name of the Amazon S3 bucket that contains the document. \n\n" +
               "    docName - The document name (must be an image, for example, book.png). \n";
   
           if (args.length != 2) {
               System.out.println(usage);
               System.exit(1);
           }
   
           String bucketName = args[0];
           String docName = args[1];
           Region region = Region.US_EAST_1;
           TextractClient textractClient = TextractClient.builder()
               .region(region)
               .credentialsProvider(ProfileCredentialsProvider.create("profile-name"))
               .build();
   
           String jobId = startDocAnalysisS3 (textractClient, bucketName, docName);
           System.out.println("Getting results for job "+jobId);
           String status = getJobResults(textractClient, jobId);
           System.out.println("The job status is "+status);
           textractClient.close();
       }
   
       // snippet-start:[textract.java2._start_doc_analysis.main]
       public static String startDocAnalysisS3 (TextractClient textractClient, String bucketName, String docName) {
   
           try {
               List<FeatureType> myList = new ArrayList<>();
               myList.add(FeatureType.TABLES);
               myList.add(FeatureType.FORMS);
   
               S3Object s3Object = S3Object.builder()
                   .bucket(bucketName)
                   .name(docName)
                   .build();
   
               DocumentLocation location = DocumentLocation.builder()
                   .s3Object(s3Object)
                   .build();
   
               StartDocumentAnalysisRequest documentAnalysisRequest = StartDocumentAnalysisRequest.builder()
                   .documentLocation(location)
                   .featureTypes(myList)
                   .build();
   
               StartDocumentAnalysisResponse response = textractClient.startDocumentAnalysis(documentAnalysisRequest);
   
               // Get the job ID
               String jobId = response.jobId();
               return jobId;
   
           } catch (TextractException e) {
               System.err.println(e.getMessage());
               System.exit(1);
           }
           return "" ;
       }
   
       private static String getJobResults(TextractClient textractClient, String jobId) {
   
           boolean finished = false;
           int index = 0 ;
           String status = "" ;
   
          try {
              while (!finished) {
                  GetDocumentAnalysisRequest analysisRequest = GetDocumentAnalysisRequest.builder()
                      .jobId(jobId)
                      .maxResults(1000)
                      .build();
   
                  GetDocumentAnalysisResponse response = textractClient.getDocumentAnalysis(analysisRequest);
                  status = response.jobStatus().toString();
   
                  if (status.compareTo("SUCCEEDED") == 0)
                      finished = true;
                  else {
                      System.out.println(index + " status is: " + status);
                      Thread.sleep(1000);
                  }
                  index++ ;
              }
   
              return status;
   
          } catch( InterruptedException e) {
              System.out.println(e.getMessage());
              System.exit(1);
          }
          return "";
       }
       // snippet-end:[textract.java2._start_doc_analysis.main]
   }

   AWS CLI

   Cette AWS CLI commande lance la détection asynchrone du texte dans un document spécifié. Elle renvoie une valeur job-id qui peut être utilisée pour récupérer les résultats de la détection.

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

   Cette AWS CLI commande renvoie les résultats d'une opération asynchrone Amazon Textract lorsqu'un. job-id

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

   Si vous accédez à la CLI sur un périphérique Windows, utilisez des guillemets doubles au lieu de guillemets simples et évitez les guillemets doubles internes par une barre oblique inverse (c’est-à-dire \) pour corriger les erreurs d’analyse que vous pourriez rencontrer. Pour un exemple, voir ci-dessous

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

   Si vous analysez un document avec l' StartDocumentAnalysisopération, vous pouvez fournir des valeurs au feature-type paramètre. L'exemple suivant montre comment inclure la QUERIES valeur dans le feature-types paramètre, puis fournir un Queries objet au queries-config paramètre.

   aws textract start-document-analysis \ 
   --document '{"S3Object":{"Bucket":"bucket","Name":"document"}}'\
    --feature-types '["QUERIES"]' \
   --queries-config '{"Queries":[{"Text":"Question"}]}'

   Python

   Remplacez profile-name dans la ligne qui crée le TextractClient par le nom de votre profil de développeur.

   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', region_name=self.region_name)
           self.sns = boto3.client('sns', region_name=self.region_name)
   
       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
   
           # For document analysis, select which features you want to obtain with the FeatureTypes argument
           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' and "Confidence" in str(block['BlockType']):
               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']))
   
           if ("Geometry") in str(block):
               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')
   
           if block["BlockType"] == "QUERY":
               print("Query info:")
               print(block["Query"])
           
           if block["BlockType"] == "QUERY_RESULT":
               print("Query answer:")
               print(block["Text"])        
                   
       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.ANALYSIS)
       analyzer.DeleteTopicandQueue()
   
   
   if __name__ == "__main__":
       main()

   Pour utiliser les différentes fonctionnalités de l'AnalyzeDocumentopération, vous devez fournir le type de fonction approprié au features-type paramètre. Par exemple, pour utiliser la fonctionnalité Requêtes, incluez la QUERIES valeur dans le feature-types paramètre, puis fournissez un Queries objet au queries-config paramètre. Pour interroger votre document, remplacez le bloc de code qui fait une demande à l'StartDocumentAnalysisopération par le bloc de code ci-dessous, puis saisissez votre requête.

   
   if self.processType == ProcessType.ANALYSIS:
               response = self.textract.start_document_analysis(
                   DocumentLocation={'S3Object': {'Bucket': self.bucket, 'Name': self.document}},
                   FeatureTypes=["TABLES", "FORMS", "QUERIES"],
                                          QueriesConfig={'Queries':[
                                              {'Text':'{}'.format("Enter query here")}
                                          ]},
                   NotificationChannel={'RoleArn': self.roleArn, 'SNSTopicArn': self.snsTopicArn})                                
                               

   Node.JS

   Dans cet exemple, remplacez la valeur de roleArn par l'ARN du rôle IAM dans Donner à Amazon Textract l'accès à votre rubrique Amazon SNS lequel vous avez enregistré. Remplacez les valeurs de bucket et document par le nom du bucket et du fichier de document que vous avez spécifiés à l'étape 2 ci-dessus. Remplacez la valeur de processType par le type de traitement que vous souhaitez utiliser sur le document d'entrée. Enfin, remplacez la valeur de REGION par la région dans laquelle votre client opère. Remplacez la valeur de profileName par le nom de votre profil de développeur.

   
   // 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";
   import {fromIni} from '@aws-sdk/credential-providers';
   
   // Set the AWS Region.
   const REGION = "region-name"; //e.g. "us-east-1"
   const profileName = "profile-name";
   // Create SNS service object.
   const textractClient = new TextractClient({region: REGION, 
     credentials: fromIni({profile: profileName,}), 
   });
   const sqsClient = new SQSClient({region: REGION, 
     credentials: fromIni({profile: profileName,}), 
   });
   const snsClient = new SNSClient({region: REGION, 
     credentials: fromIni({profile: profileName,}), 
   });
   
   // 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. Exécutez le code. L’opération peut prendre un certain temps pour s’exécuter. Une fois l'opération terminée, une liste de blocs pour le texte détecté ou analysé s'affiche.