Análise do texto do documento com o Amazon Textract

Para analisar o texto em um documento (API)

1. Se ainda não tiver feito isso:

   1. Dê a um usuário AmazonTextractFullAccess as AmazonS3ReadOnlyAccess permissões e. Para obter mais informações, consulte Etapa 1: Configurar uma conta da AWS e criar um usuário.

   2. Instale e configure o AWS CLI e os AWS SDKs. Para obter mais informações, consulte Etapa 2: configurar o AWS CLI and AWS SDKs.

2. Faça upload de uma imagem que contenha um documento para seu bucket do S3.

   Para obter instruções, consulte Como fazer upload de objetos no Amazon S3 no Guia do usuário do Amazon Simple Storage Service.

3. Use os exemplos a seguir para chamar a operação AnalyzeDocument.

   Java

   O código de exemplo a seguir exibe o documento e as caixas ao redor dos itens detectados.

   Na funçãomain, substitua os valores de bucket e document pelos nomes do bucket do Amazon S3 e da imagem do documento que você usou na etapa 2. Substitua o valor de credentialsProvider com o nome do seu perfil de desenvolvedor.

   
   //Loads document from S3 bucket. Displays the document and polygon around detected lines of text.
   import java.awt.*;
   import java.awt.image.BufferedImage;
   import java.util.List;
   import javax.imageio.ImageIO;
   import javax.swing.*;
   import com.amazonaws.services.s3.AmazonS3;
   import com.amazonaws.services.s3.AmazonS3ClientBuilder;
   import com.amazonaws.services.s3.model.S3ObjectInputStream;
   import com.amazonaws.auth.profile.ProfileCredentialsProvider;
   import com.amazonaws.services.textract.AmazonTextract;
   import com.amazonaws.services.textract.AmazonTextractClientBuilder;
   import com.amazonaws.services.textract.model.AnalyzeDocumentRequest;
   import com.amazonaws.services.textract.model.AnalyzeDocumentResult;
   import com.amazonaws.services.textract.model.Block;
   import com.amazonaws.services.textract.model.BoundingBox;
   import com.amazonaws.services.textract.model.Document;
   import com.amazonaws.services.textract.model.S3Object;
   import com.amazonaws.services.textract.model.Point;
   import com.amazonaws.services.textract.model.Relationship;
   import com.amazonaws.client.builder.AwsClientBuilder.EndpointConfiguration;
   
   public class AnalyzeDocument extends JPanel {
   
       private static final long serialVersionUID = 1L;
   
       BufferedImage image;
   
       AnalyzeDocumentResult result;
   
       public AnalyzeDocument(AnalyzeDocumentResult documentResult, BufferedImage bufImage) throws Exception {
           super();
   
           result = documentResult; // Results of text detection.
           image = bufImage; // The image containing the document.
   
       }
   
       // Draws the image and text bounding box.
       public void paintComponent(Graphics g) {
   
           int height = image.getHeight(this);
           int width = image.getWidth(this);
   
           Graphics2D g2d = (Graphics2D) g; // Create a Java2D version of g.
   
           // Draw the image.
           g2d.drawImage(image, 0, 0, image.getWidth(this), image.getHeight(this), this);
   
           // Iterate through blocks and display bounding boxes around everything.
   
           List<Block> blocks = result.getBlocks();
           for (Block block : blocks) {
               DisplayBlockInfo(block);
               switch(block.getBlockType()) {
               
               case "KEY_VALUE_SET":
                   if (block.getEntityTypes().contains("KEY")){
                       ShowBoundingBox(height, width, block.getGeometry().getBoundingBox(), g2d, new Color(255,0,0));
                   }
                   else {  //VALUE
                       ShowBoundingBox(height, width, block.getGeometry().getBoundingBox(), g2d, new Color(0,255,0));
                   }
                   break;
               case "TABLE":
                   ShowBoundingBox(height, width, block.getGeometry().getBoundingBox(), g2d, new Color(0,0,255));
                   break;
               case "CELL":
                   ShowBoundingBox(height, width, block.getGeometry().getBoundingBox(), g2d, new Color(255,255,0));
                   break;
               case "SELECTION_ELEMENT":
                   if (block.getSelectionStatus().equals("SELECTED"))
                       ShowSelectedElement(height, width, block.getGeometry().getBoundingBox(), g2d, new Color(0,0,255));
                   break;    
               default:
                   //PAGE, LINE & WORD
                   //ShowBoundingBox(height, width, block.getGeometry().getBoundingBox(), g2d, new Color(200,200,0));
               }
           }
   
            // uncomment to show polygon around all blocks
            //ShowPolygon(height,width,block.getGeometry().getPolygon(),g2d);
         
         
       }
   
       // Show bounding box at supplied location.
       private void ShowBoundingBox(int imageHeight, int imageWidth, BoundingBox box, Graphics2D g2d, Color color) {
   
           float left = imageWidth * box.getLeft();
           float top = imageHeight * box.getTop();
   
           // Display bounding box.
           g2d.setColor(color);
           g2d.drawRect(Math.round(left), Math.round(top),
                   Math.round(imageWidth * box.getWidth()), Math.round(imageHeight * box.getHeight()));
   
       }
       private void ShowSelectedElement(int imageHeight, int imageWidth, BoundingBox box, Graphics2D g2d, Color color) {
   
           float left = imageWidth * box.getLeft();
           float top = imageHeight * box.getTop();
   
           // Display bounding box.
           g2d.setColor(color);
           g2d.fillRect(Math.round(left), Math.round(top),
                   Math.round(imageWidth * box.getWidth()), Math.round(imageHeight * box.getHeight()));
   
       }
   
       // Shows polygon at supplied location
       private void ShowPolygon(int imageHeight, int imageWidth, List<Point> points, Graphics2D g2d) {
   
           g2d.setColor(new Color(0, 0, 0));
           Polygon polygon = new Polygon();
   
           // Construct polygon and display
           for (Point point : points) {
               polygon.addPoint((Math.round(point.getX() * imageWidth)),
                       Math.round(point.getY() * imageHeight));
           }
           g2d.drawPolygon(polygon);
       }
       //Displays information from a block returned by text detection and text analysis
       private void DisplayBlockInfo(Block block) {
           System.out.println("Block Id : " + block.getId());
           if (block.getText()!=null)
               System.out.println("    Detected text: " + block.getText());
           System.out.println("    Type: " + block.getBlockType());
           
           if (block.getBlockType().equals("PAGE") !=true) {
               System.out.println("    Confidence: " + block.getConfidence().toString());
           }
           if(block.getBlockType().equals("CELL"))
           {
               System.out.println("    Cell information:");
               System.out.println("        Column: " + block.getColumnIndex());
               System.out.println("        Row: " + block.getRowIndex());
               System.out.println("        Column span: " + block.getColumnSpan());
               System.out.println("        Row span: " + block.getRowSpan());
   
           }
           
           System.out.println("    Relationships");
           List<Relationship> relationships=block.getRelationships();
           if(relationships!=null) {
               for (Relationship relationship : relationships) {
                   System.out.println("        Type: " + relationship.getType());
                   System.out.println("        IDs: " + relationship.getIds().toString());
               }
           } else {
               System.out.println("        No related Blocks");
           }
   
           System.out.println("    Geometry");
           System.out.println("        Bounding Box: " + block.getGeometry().getBoundingBox().toString());
           System.out.println("        Polygon: " + block.getGeometry().getPolygon().toString());
           
           List<String> entityTypes = block.getEntityTypes();
           
           System.out.println("    Entity Types");
           if(entityTypes!=null) {
               for (String entityType : entityTypes) {
                   System.out.println("        Entity Type: " + entityType);
               }
           } else {
               System.out.println("        No 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("    Page: " + block.getPage());            
           System.out.println();
       }
   
       public static void main(String arg[]) throws Exception {
   
           // The S3 bucket and document
           String document = "";
           String bucket = "";
           
           // set provider credentials
           AWSCredentialsProvider credentialsProvider = new ProfileCredentialsProvider("default");
   
           AmazonS3 s3client = AmazonS3ClientBuilder.standard().withCredentials(credentialsProvider)
                   .withEndpointConfiguration( 
                           new EndpointConfiguration("https://s3.amazonaws.com","us-east-1"))
                   .build();
           
                  
           // Get the document from S3
           com.amazonaws.services.s3.model.S3Object s3object = s3client.getObject(bucket, document);
           S3ObjectInputStream inputStream = s3object.getObjectContent();
           BufferedImage image = ImageIO.read(inputStream);
   
           // Call AnalyzeDocument 
           EndpointConfiguration endpoint = new EndpointConfiguration(
                   "https://textract.us-east-1.amazonaws.com", "us-east-1");
           AmazonTextract client = AmazonTextractClientBuilder.standard().withCredentials(credentialsProvider)
                   .withEndpointConfiguration(endpoint).build();
   
                   
           AnalyzeDocumentRequest request = new AnalyzeDocumentRequest()
                   .withFeatureTypes("TABLES","FORMS","SIGNATURES")
                    .withDocument(new Document().
                           withS3Object(new S3Object().withName(document).withBucket(bucket)));
   
   
           AnalyzeDocumentResult result = client.analyzeDocument(request);
   
           // Create frame and panel.
           JFrame frame = new JFrame("RotateImage");
           frame.setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE);
           AnalyzeDocument panel = new AnalyzeDocument(result, image);
           panel.setPreferredSize(new Dimension(image.getWidth(), image.getHeight()));
           frame.setContentPane(panel);
           frame.pack();
           frame.setVisible(true); 
   
       }
   }

   Java V2

   O código de exemplo a seguir exibe o documento e as caixas ao redor das linhas do texto detectado.

   Na funçãomain, substitua os valores de bucket e document pelos nomes do bucket e do documento do Amazon S3 que você usou na etapa 2. Substitua profile-name na linha que cria TextractClient o pelo nome do seu perfil de desenvolvedor.

   import software.amazon.awssdk.auth.credentials.ProfileCredentialsProvider;
   import software.amazon.awssdk.core.SdkBytes;
   import software.amazon.awssdk.regions.Region;
   import software.amazon.awssdk.services.textract.TextractClient;
   import software.amazon.awssdk.services.textract.model.AnalyzeDocumentRequest;
   import software.amazon.awssdk.services.textract.model.Document;
   import software.amazon.awssdk.services.textract.model.FeatureType;
   import software.amazon.awssdk.services.textract.model.S3Object;
   import software.amazon.awssdk.services.textract.model.AnalyzeDocumentResponse;
   import software.amazon.awssdk.services.textract.model.Block;
   import software.amazon.awssdk.services.textract.model.TextractException;
   import java.io.File;
   import java.io.FileInputStream;
   import java.io.FileNotFoundException;
   import java.io.InputStream;
   import java.util.ArrayList;
   import java.util.Iterator;
   import java.util.List;
   // snippet-end:[textract.java2._analyze_doc.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 AnalyzeDocument {
   
       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, i.e., 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();
   
           analyzeDoc(textractClient, bucketName, docName);
           textractClient.close();
       }
   
       // snippet-start:[textract.java2._analyze_doc.main]
       public static void analyzeDoc(TextractClient textractClient, String bucketName, String docName) {
   
           try {
               S3Object s3Object = S3Object.builder()
                       .bucket(bucketName)
                       .name(docName)
                       .build();
   
                   // Create a Document object and reference the s3Object instance
                   Document myDoc = Document.builder()
                       .s3Object(s3Object)
                       .build();
   
               List<FeatureType> featureTypes = new ArrayList<FeatureType>();
               featureTypes.add(FeatureType.FORMS);
               featureTypes.add(FeatureType.TABLES);
   
               AnalyzeDocumentRequest analyzeDocumentRequest = AnalyzeDocumentRequest.builder()
                       .featureTypes(featureTypes)
                       .document(myDoc)
                       .build();
   
               AnalyzeDocumentResponse analyzeDocument = textractClient.analyzeDocument(analyzeDocumentRequest);
               List<Block> docInfo = analyzeDocument.blocks();
               Iterator<Block> blockIterator = docInfo.iterator();
   
               while(blockIterator.hasNext()) {
                   Block block = blockIterator.next();
                   System.out.println("The block type is " +block.blockType().toString());
               }
   
           } catch (TextractException e) {
   
               System.err.println(e.getMessage());
               System.exit(1);
           }
       }
       // snippet-end:[textract.java2._analyze_doc.main]
   }

   AWS CLI

   Esse AWS CLI comando exibe a saída JSON para a operação analyze-document CLI.

   Substitua os valores Name de Bucket e pelos nomes do bucket e do documento do Amazon S3 que você usou na etapa 2. profile-nameSubstitua pelo nome de um perfil que possa assumir a função e region pela região na qual você deseja executar o código.

   aws textract analyze-document \
       --document '{"S3Object":{"Bucket":"bucket","Name":"document"}}' \
       --feature-types '["TABLES","FORMS","SIGNATURES"]' \
       --profile profile-name \
       --region region

   Para usar o recurso Consultas, inclua o valor 'QUERIES' no parâmetro 'feature-types' e forneça um Queries objeto para o parâmetro queries-config ''. Para usar um adaptador, inclua quaisquer AdapterId Version s e s em uma lista Adapters fornecida ao AdapterConfig parâmetro.

   aws textract analyze-document \
   --document '{"S3Object":{"Bucket":"bucket","Name":"document"}}'\
    --feature-types '["QUERIES"]' \
   --queries-config '{"Queries":[{"Text":"Question"}]}' \
   --profile profile-name \
   --region region
   --adapters-config '{"Adapters": [{"AdapterId": "AdapterId", "Version": "1"]}' 

   Python

   O código de exemplo a seguir exibe o documento e as caixas ao redor dos itens detectados.

   Na funçãomain, substitua os valores de bucket e document pelos nomes do bucket e do documento do Amazon S3 que você usou na etapa 2. profile-nameSubstitua pelo nome de um perfil que possa assumir a função e region pela região na qual você deseja executar o código. Para usar um adaptador, inclua quaisquer AdapterId Version s e s em uma lista Adapters fornecida ao AdapterConfig parâmetro.

   #Analyzes text in a document stored in an S3 bucket. Display polygon box around text and angled text 
   import boto3
   import io
   from PIL import Image, ImageDraw
   
   def ShowBoundingBox(draw,box,width,height,boxColor):
                
       left = width * box['Left']
       top = height * box['Top'] 
       draw.rectangle([left,top, left + (width * box['Width']), top +(height * box['Height'])],outline=boxColor)   
   
   def ShowSelectedElement(draw,box,width,height,boxColor):
                
       left = width * box['Left']
       top = height * box['Top'] 
       draw.rectangle([left,top, left + (width * box['Width']), top +(height * box['Height'])],fill=boxColor)  
   
   # Displays information about a block returned by text detection and text analysis
   def DisplayBlockInformation(block):
       print('Id: {}'.format(block['Id']))
       if 'Text' in block:
           print('    Detected: ' + block['Text'])
       print('    Type: ' + block['BlockType'])
      
       if 'Confidence' in block:
           print('    Confidence: ' + "{:.2f}".format(block['Confidence']) + "%")
   
       if block['BlockType'] == 'CELL':
           print("    Cell information")
           print("        Column:" + str(block['ColumnIndex']))
           print("        Row:" + str(block['RowIndex']))
           print("        Column Span:" + str(block['ColumnSpan']))
           print("        RowSpan:" + str(block['ColumnSpan']))    
       
       if 'Relationships' in block:
           print('    Relationships: {}'.format(block['Relationships']))
       print('    Geometry: ')
       print('        Bounding Box: {}'.format(block['Geometry']['BoundingBox']))
       print('        Polygon: {}'.format(block['Geometry']['Polygon']))
       
       if block['BlockType'] == "KEY_VALUE_SET":
           print ('    Entity Type: ' + block['EntityTypes'][0])
       
       if block['BlockType'] == 'SELECTION_ELEMENT':
           print('    Selection element detected: ', end='')
   
           if block['SelectionStatus'] =='SELECTED':
               print('Selected')
           else:
               print('Not selected')    
       
       if 'Page' in block:
           print('Page: ' + block['Page'])
       print()
   
   def process_text_analysis(s3_connection, client, bucket, document):
   
       # Get the document from S3                          
       s3_object = s3_connection.Object(bucket,document)
       s3_response = s3_object.get()
   
       stream = io.BytesIO(s3_response['Body'].read())
       image=Image.open(stream)
   
       # Analyze the document
       image_binary = stream.getvalue()
       response = client.analyze_document(Document={'Bytes': image_binary},
           FeatureTypes=["TABLES", "FORMS", "SIGNATURES"])
   
       ### Uncomment to process using S3 object ###
       #response = client.analyze_document(
       #    Document={'S3Object': {'Bucket': bucket, 'Name': document}},
       #    FeatureTypes=["TABLES", "FORMS", "SIGNATURES"])
   
       ### Uncomment to analyze a local file ###
       # with open("pathToFile", 'rb') as img_file:
           ### To display image using PIL ###
       #    image = Image.open()
           ### Read bytes ###
       #    img_bytes = img_file.read()
       #    response = client.analyze_document(Document={'Bytes': img_bytes}, FeatureTypes=["TABLES", "FORMS", "SIGNATURES"])
       
       #Get the text blocks
       blocks=response['Blocks']
       width, height =image.size    
       print ('Detected Document Text')
      
       # Create image showing bounding box/polygon the detected lines/text
       for block in blocks:
           DisplayBlockInformation(block)    
           draw=ImageDraw.Draw(image)
   
           # Draw bounding boxes for different detected response objects
           if block['BlockType'] == "KEY_VALUE_SET":
               if block['EntityTypes'][0] == "KEY":
                   ShowBoundingBox(draw, block['Geometry']['BoundingBox'],width,height,'red')
               else:
                   ShowBoundingBox(draw, block['Geometry']['BoundingBox'],width,height,'green')             
           if block['BlockType'] == 'TABLE':
               ShowBoundingBox(draw, block['Geometry']['BoundingBox'],width,height, 'blue')
           if block['BlockType'] == 'CELL':
               ShowBoundingBox(draw, block['Geometry']['BoundingBox'],width,height, 'yellow')
           if block['BlockType'] == 'SELECTION_ELEMENT':
               if block['SelectionStatus'] =='SELECTED':
                   ShowSelectedElement(draw, block['Geometry']['BoundingBox'],width,height, 'blue')    
               
       # Display the image
       image.show()
       return len(blocks)
   
   def main():
   
       session = boto3.Session(profile_name='profile-name')
       s3_connection = session.resource('s3')
       client = session.client('textract', region_name='region')
       bucket = ""
       document = ""
       block_count=process_text_analysis(s3_connection, client, bucket, document)
       print("Blocks detected: " + str(block_count))
       
   if __name__ == "__main__":
       main()
   

   Para usar diferentes recursos da AnalyzeDocument operação, você fornece o tipo de recurso adequado ao features-type parâmetro. Por exemplo, para usar o recurso Consultas, inclua o QUERIES valor no feature-types parâmetro e, em seguida, forneça um Queries objeto para o queries-config parâmetro. Para consultar seu documento, adicione a query_document função no código a seguir ao exemplo de código anterior. Em seguida, inclua a question variável e a linha que invoca a query_document função para a função anteriormain.

   def query_document(client, bucket, document, question):
       # Analyze the document
       response = client.analyze_document(Document={'S3Object': {'Bucket': bucket, 'Name': document}},
                                          FeatureTypes=["TABLES", "FORMS", "QUERIES"],
                                          QueriesConfig={'Queries':[
                                              {'Text':'{}'.format(question)}
                                          ]})
       
       for block in response['Blocks']:
           if block["BlockType"] == "QUERY":
               print("Query info:")
               print(block["Query"])
           if block["BlockType"] == "QUERY_RESULT":
               print("Query answer:")
               print(block["Text"])
   
   question = "query here"
   query_document(client, bucket, document, question)

   Node.js

   O código de exemplo a seguir exibe o documento e as caixas ao redor dos itens detectados.

   No código a seguir, substitua os valores de bucket e photo pelos nomes do bucket e do documento do Amazon S3 que você usou na etapa 2. Substitua o valor de region pela região associada à sua conta. Substitua o valor de credentials com o nome do seu perfil de desenvolvedor.

   // Import required AWS SDK clients and commands for Node.js
   import { AnalyzeDocumentCommand } from  "@aws-sdk/client-textract";
   import  { TextractClient } from "@aws-sdk/client-textract";
   import {fromIni} from '@aws-sdk/credential-providers';
   
   // Set the AWS Region.
   const REGION = "region"; //e.g. "us-east-1"
   const profileName = "default";
   
   // Create SNS service object.
   const textractClient = new TextractClient({region: REGION, 
     credentials: fromIni({profile: profileName,}), 
   });
   
   const bucket = 'buckets'
   const photo = 'photo'
   
   // Set params
   const params = {
       Document: {
         S3Object: {
           Bucket: bucket,
           Name: photo
         },
       },
       FeatureTypes: ['TABLES', 'FORMS', 'SIGNATURES'],
     }
   
   const displayBlockInfo = async (response) => {
       try {
           response.Blocks.forEach(block => {
               console.log(`ID: ${block.Id}`)
               console.log(`Block Type: ${block.BlockType}`)
               if ("Text" in block && block.Text !== undefined){
                   console.log(`Text: ${block.Text}`)
               }
               else{}
               if ("Confidence" in block && block.Confidence !== undefined){
                   console.log(`Confidence: ${block.Confidence}`)
               }
               else{}
               if (block.BlockType == 'CELL'){
                   console.log("Cell info:")
                   console.log(`   Column Index - ${block.ColumnIndex}`)
                   console.log(`   Row - ${block.RowIndex}`)
                   console.log(`   Column Span - ${block.ColumnSpan}`)
                   console.log(`   Row Span - ${block.RowSpan}`)
               }
               if ("Relationships" in block && block.Relationships !== undefined){
                   console.log(block.Relationships)
                   console.log("Geometry:")
                   console.log(`   Bounding Box - ${JSON.stringify(block.Geometry.BoundingBox)}`)
                   console.log(`   Polygon - ${JSON.stringify(block.Geometry.Polygon)}`)
               }
               console.log("-----")
           });
         } catch (err) {
           console.log("Error", err);
         }
   }
   
   const analyze_document_text = async () => {
       try {
           const analyzeDoc = new AnalyzeDocumentCommand(params);
           const response = await textractClient.send(analyzeDoc);
           //console.log(response)
           displayBlockInfo(response)
           return response; // For unit tests.
         } catch (err) {
           console.log("Error", err);
         }
   }
   
   analyze_document_text()

   .NET

   O exemplo a seguir exibe o texto detectado e suas relações em uma lista.

   Substitua os valores document de bucket e pelos nomes do bucket do Amazon S3 e da imagem do documento que você usou na etapa 2.

   
   using System;
   using System.Linq;
   using System.Reflection.Emit;
   using Amazon.Runtime;
   using Amazon.Textract;
   using Amazon.Textract.Model;
   
   namespace TextractAnalyzeExpense
   {
       class Program 
       {
           static async Task Main()
           {
               String document = "document";
               String bucket = "bucket";
   
               AmazonTextractClient textractClient = new AmazonTextractClient();
   
               AnalyzeExpenseRequest analyzeExpenseRequest = new AnalyzeExpenseRequest()
               {
                   Document = new Document()
                   {
                       S3Object = new S3Object()
                       {
                           Name = document,
                           Bucket = bucket
                       }
                   }
               };
   
               try
               {
                   var ExpenseAnalysis = await textractClient.AnalyzeExpenseAsync(analyzeExpenseRequest);
                   Console.WriteLine("Line Items:");
                   foreach (ExpenseDocument expenseDocument in ExpenseAnalysis.ExpenseDocuments)
                   {
                       Console.WriteLine("Line Items:");
                      foreach(LineItemGroup linegroup in expenseDocument.LineItemGroups)
                      {
                       PrintLineItems.LineItemPrinter.LineItemParse(linegroup);
                      }
                      
                      Console.WriteLine("Summary:\n");
                      foreach(ExpenseField summary in expenseDocument.SummaryFields)
                      {
                       if (summary.LabelDetection is not null) 
                       {
                           Console.WriteLine(summary.LabelDetection.Text);
                       }
                      if (summary.ValueDetection is not null) 
                       {
                           Console.WriteLine(summary.ValueDetection.Text);
                       }
   
                      }
                   }
   
                   
               }
               catch (Exception e)
               {
                   Console.WriteLine(e.Message);
               }
           }
       
       }
   }
   
   namespace PrintLineItems 
   {
       class LineItemPrinter
       {
           public static void LineItemParse(LineItemGroup lineitemgroup) 
           {
               foreach(LineItemFields lineitem in lineitemgroup.LineItems) {
                   foreach(ExpenseField expense in lineitem.LineItemExpenseFields){
                      if (expense.LabelDetection is not null) 
                      {
                       Console.WriteLine(expense.LabelDetection.Text);
                      }
                      if (expense.ValueDetection is not null) 
                      {
                       Console.WriteLine(expense.ValueDetection.Text);
                      }
   
                   }    
   
               }
   
           }
       }
   }
                           

4. Execute o exemplo. Os exemplos em Python e Java exibem a imagem do documento com as seguintes caixas delimitadoras coloridas:

   • Vermelho — KEY Block objetos

   • Verde — objetos do bloco VALUE

   • Azul — Objetos do bloco TABLE

   • Amarelo — objetos CELL Block

   Os elementos de seleção selecionados são preenchidos com azul.

   O AWS CLI exemplo exibe somente a saída JSON da AnalyzeDocument operação.