Detección o análisis de texto en un documento de varias páginas

Este procedimiento muestra cómo detectar o analizar texto en un documento de varias páginas mediante operaciones de detección de Amazon Textract Texact, un documento almacenado en un depósito de Amazon S3, un tema de Amazon SNS y una cola de Amazon SQS. El procesamiento de documentos de varias páginas es asíncrona. Para obtener más información, consulte Llamar a operaciones asíncronas de Amazon Textract.

Puede elegir el tipo de procesamiento que desea que realice el código: detección de texto, análisis de texto o análisis de gastos.

Los resultados del procesamiento se devuelven en una matriz deBlockobjetos que varían según el tipo de procesamiento que utilice.

Para detectar texto o analizar documentos de varias páginas, haga lo siguiente:

Cree el tema de Amazon SNS y la cola de Amazon SQS.
Suscriba la cola de al tema de.
Conceda permiso al tema de para enviar mensajes a la cola de.
Comience a procesar el documento. Utilice la operación adecuada para el tipo de análisis elegido:
- StartDocumentTextDetectionpara tareas de detección de texto.
- StartDocumentAnalysispara tareas de análisis de texto.
- StartExpenseAnalysispara tareas de análisis de gastos.
Obtenga el estado de realización a partir de la cola de Amazon SQS. El código de ejemplo hace un seguimiento del identificador del trabajo (JobId) devuelto por elStart. Solo obtiene los resultados de identificadores de trabajo coincidentes que se leen desde el estado de realización. Esto es importante si otras aplicaciones están utilizando la misma cola de y tema de. Para simplificar, en el ejemplo se eliminan los trabajos que no coinciden. Plantéese añadir los trabajos eliminados a una cola de mensajes erróneos de Amazon SQS para examinarlos posteriormente.
Obtenga y muestre los resultados del procesamiento llamando a la operación adecuada para el tipo de análisis elegido:
- GetDocumentTextDetectionpara tareas de detección de texto.
- GetDocumentAnalysispara tareas de análisis de texto.
- GetExpenseAnalysispara tareas de análisis de gastos.
Elimine el tema de Amazon SNS y la cola de Amazon SQS.

Realización de operaciones asíncronas

El código de ejemplo de este procedimiento se proporciona en Java, Python yAWS CLI. Antes de comenzar, instale el correspondienteAWSSDK. Para obtener más información, consulte Paso 2: Configurar laAWS CLIyAWSSDK de.

Para detectar o analizar texto en un documento de varias páginas

Configure el acceso de usuario a Amazon Textract y configure el acceso de Amazon Textract Texact a Amazon SNS. Para obtener más información, consulte Configuración de Amazon Textract Texact para operaciones asíncronas. Para completar este procedimiento, se necesita un archivo de documento de varias páginas en formato PDF. Omita los pasos 3 a 6, ya que el código de ejemplo crea y configura el tema de Amazon SNS y la cola de Amazon SQS. Si es complejotEn el ejemplo de la CLI, no es necesario configurar una cola de SQS.
Cargue un archivo de documento de varias páginas en formato PDF o TIFF a su bucket de Amazon S3. (También se pueden procesar documentos de una sola página en formato JPEG, PNG, TIFF o PDF).

Para obtener instrucciones, consulteCarga de objetos en Amazon S3en laAmazon Simple Storage Service.

Utilice lo siguienteAWS SDK for Java, SDK for Python (Boto3) oAWS CLIcódigo para detectar texto o analizar texto en un documento de varias páginas. En el navegadormainfunción:

Sustituir el valor deroleArncon el ARN del rol de IAM que ha guardado enConceder acceso a Amazon Textract a su tema de Amazon SNS.
Sustituir los valores debucketydocumentcon el nombre del bucket de y el nombre del archivo de documento que especificó en el paso 2.
Sustituya el valor de latypeparámetro de entrada delProcessDocumentfunción con el tipo de procesamiento que desea realizar. UsarProcessType.DETECTIONpara detectar texto. UsarProcessType.ANALYSISpara analizar texto.
Para el ejemplo de Python, sustituya el valor deregion_namecon la región en la que opera su cliente.

Para el registroAWS CLIejemplo, haga lo siguiente:

Al llamarStartDocumentTextDetection, sustituya el valor debucket-namecon el nombre de su bucket de S3 y reemplacefile-namecon el nombre del archivo que especificó en el paso 2. Especifique la región de su depósito reemplazandoregion-namecon el nombre de su región. Tenga en cuenta que el ejemplo de la CLI no utiliza SQS.
Al llamarGetDocumentTextDetectionreplacejob-id-numberconjob-iddevuelto porStartDocumentTextDetection. Especifique la región de su depósito reemplazandoregion-namecon el nombre de su región.

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

EsteAWS CLIinicia la detección asincrónica del texto de un documento especificado. Este método devuelve un objetojob-idque se puede utilizar para recuperar los resultados de la detección.


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

EsteAWS CLIdevuelve los resultados de una operación asíncrona de Amazon Textract cuando se proporciona con unjob-id.


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

Si está accediendo a la CLI en un dispositivo Windows, utilice comillas dobles en lugar de comillas simples y escapa de las comillas dobles internas mediante barra invertida (es decir,\) para corregir cualquier error de analizador que pueda surgir. Para un ejemplo, consulte lo siguiente


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

En este ejemplo, reemplace el valor deroleArncon el ARN del rol de IAM que ha guardado enConceder acceso a Amazon Textract a su tema de Amazon SNS. Sustituir los valores debucketydocumentcon el nombre del bucket de y el nombre del archivo de documento que especificó en el paso 2 anterior. Sustituir el valor deprocessTypecon el tipo de procesamiento que quieres utilizar en el documento de entrada. Por último, sustituya el valor deREGIONcon la región en la que opera su cliente.


 // 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()

Ejecute el código. La operación podría llevar algún tiempo. Una vez terminada, se muestra una lista de bloques para el texto detectado o analizado.

Aviso JavaScript está desactivado o no está disponible en su navegador.

Para utilizar la documentación de AWS, debe estar habilitado JavaScript. Para obtener más información, consulte las páginas de ayuda de su navegador.

Convenciones del documento

Configuración de operaciones asincrónicas

Notificación de resultados de Amazon Textract