Análisis del texto de un documento con Amazon Textract

Para analizar el texto de un documento (API)

1. Si aún no lo ha hecho:

   1. Otorgue al usuario AmazonS3ReadOnlyAccess los permisos AmazonTextractFullAccess y Para obtener más información, consulte Paso 1: Configurar una cuenta de AWS y crear un usuario.

   2. Instale y configure los SDK AWS CLI y los AWS SDK. Para obtener más información, consulte Paso 2: configurar el AWS CLI and AWS SDK.

2. Sube una imagen que contenga un documento a tu bucket de S3.

   Para ver las instrucciones, consulte Carga de objetos en Amazon S3 en la Guía del usuario de Amazon Simple Storage Service.

3. Consulte los siguientes ejemplos para llamar a la operación AnalyzeDocument.

   Java

   El siguiente código de ejemplo muestra el documento y las casillas alrededor de los elementos detectados.

   En la funciónmain, sustituya los valores de bucket y por document los nombres del bucket de Amazon S3 y la imagen del documento que utilizó en el paso 2. Sustituya el valor de credentialsProvider de por el nombre de su perfil de desarrollador.

   
   //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

   El siguiente código de ejemplo muestra el documento y los cuadros alrededor de las líneas del texto detectado.

   En la funciónmain, sustituya los valores de bucket y document por los nombres del bucket y el documento de Amazon S3 que utilizó en el paso 2. Sustitúyalo profile-name en la línea que lo TextractClient crea por el nombre de su perfil de desarrollador.

   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

   Este AWS CLI comando muestra la salida JSON de la operación de la analyze-document CLI.

   Sustituya los valores de Bucket y Name por los nombres del bucket y el documento de Amazon S3 que utilizó en el paso 2. profile-nameSustitúyalo por el nombre de un perfil que pueda asumir el rol y region por la región en la que desea ejecutar el código.

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

   Para utilizar la función de consultas, incluya el valor 'QUERIES' en el parámetro 'feature-types' y, a continuación, proporcione un Queries objeto al parámetro queries-config ''. Para usar un adaptador, incluya cualquier AdapterId s y Version s en la lista de los Adapters proporcionados al AdapterConfig parámetro.

   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

   El código de ejemplo siguiente muestra el documento y los recuadros alrededor de los elementos detectados.

   En la funciónmain, sustituya los valores de bucket y document por los nombres del bucket y el documento de Amazon S3 que utilizó en el paso 2. profile-nameSustitúyalo por el nombre de un perfil que pueda asumir la función y region por la región en la que desea ejecutar el código. Para usar un adaptador, incluya cualquier AdapterId s y Version s en la lista de los Adapters proporcionados al AdapterConfig parámetro.

   #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 utilizar diferentes funciones de la AnalyzeDocument operación, debe proporcionar el tipo de función adecuado al features-type parámetro. Por ejemplo, para usar la función de consultas, incluya el QUERIES valor en el feature-types parámetro y, a continuación, proporcione un Queries objeto al queries-config parámetro. Para consultar el documento, añada la query_document función del código siguiente al ejemplo de código anterior. A continuación, incluya la question variable y la línea que invoca la query_document función en la main función anterior.

   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

   El código de ejemplo siguiente muestra el documento y los cuadros que rodean los elementos detectados.

   En el siguiente código, sustituya los valores de bucket y photo por los nombres del bucket y el documento de Amazon S3 que utilizó en el paso 2. Sustituya el valor de region por la región asociada a su cuenta. Sustituya el valor de credentials de por el nombre de su perfil de desarrollador.

   // 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

   El siguiente ejemplo muestra el texto detectado y sus relaciones en una lista.

   Sustituya los valores de bucket y document por los nombres del bucket de Amazon S3 y la imagen del documento que utilizó en el paso 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. Ejecute el ejemplo. Los ejemplos de Python y Java muestran la imagen del documento con los siguientes cuadros delimitadores de colores:

   • Rojo: los objetos KEY Block

   • Verde: VALOR: bloquea objetos

   • Azul: tabla: bloquea objetos

   • Amarillo: CELL: bloquea objetos

   Los elementos de selección seleccionados se rellenan de azul.

   El AWS CLI ejemplo muestra solo la salida JSON de la AnalyzeDocument operación.