Menganalisis Teks Dokumen dengan Amazon Textract

Untuk menganalisis teks dalam dokumen (API)

1. Jika belum:

   1. Berikan pengguna AmazonS3ReadOnlyAccess izin AmazonTextractFullAccess dan. Untuk informasi selengkapnya, lihat Langkah 1: Siapkan Akun AWS dan Buat Pengguna.

   2. Instal dan konfigurasikan AWS CLI dan AWS SDK. Untuk informasi selengkapnya, lihat Langkah 2: Siapkan AWS CLI and AWS SDK.

2. Unggah gambar yang berisi dokumen ke bucket S3 Anda.

   Untuk petunjuk, lihat Meng unggah Objek ke Amazon S3 di Panduan Pengguna Layanan Penyimpanan Sederhana Amazon.

3. Gunakan contoh berikut untuk memanggil operasi AnalyzeDocument.

   Java

   Kode contoh berikut menampilkan dokumen dan kotak di sekitar item yang terdeteksi.

   Dalam fungsimain, ganti nilai bucket dan document dengan nama bucket Amazon S3 dan gambar dokumen yang Anda gunakan pada langkah 2. Ganti nilai credentialsProvider dengan nama profil pengembang Anda.

   
   //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

   Kode contoh berikut menampilkan dokumen dan kotak di sekitar baris teks yang terdeteksi.

   Dalam fungsimain, ganti nilai bucket dan document dengan nama bucket dan dokumen Amazon S3 yang Anda gunakan pada langkah 2. Ganti profile-name di baris yang membuat TextractClient dengan nama profil pengembang Anda.

   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

   Per AWS CLI intah ini menampilkan output JSON untuk operasi analyze-document CLI.

   Ganti nilai Bucket dan Name dengan nama bucket dan dokumen Amazon S3 yang Anda gunakan pada langkah 2. Ganti profile-name dengan nama profil yang dapat mengambil peran dan region dengan wilayah di mana Anda ingin menjalankan kode.

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

   Untuk menggunakan fitur Queries, sertakan nilai QUERIES '' dalam parameter feature-types '' dan kemudian berikan Queries objek ke parameter queries-config ''. Untuk menggunakan adaptor, sertakan AdapterId s dan Version s apa pun dalam daftar yang Adapters disediakan untuk AdapterConfig parameter.

   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

   Kode contoh berikut menampilkan dokumen dan kotak di sekitar item yang terdeteksi.

   Dalam fungsimain, ganti nilai bucket dan document dengan nama bucket dan dokumen Amazon S3 yang Anda gunakan pada langkah 2. Ganti profile-name dengan nama profil yang dapat mengambil peran dan region dengan wilayah di mana Anda ingin menjalankan kode. Untuk menggunakan adaptor, sertakan AdapterId s dan Version s apa pun dalam daftar yang Adapters disediakan untuk AdapterConfig parameter.

   #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()
   

   Untuk menggunakan fitur AnalyzeDocument operasi yang berbeda, Anda memberikan jenis fitur yang tepat untuk features-type parameter. Misalnya, untuk menggunakan fitur Queries, sertakan QUERIES nilai dalam feature-types parameter dan kemudian berikan Queries objek ke queries-config parameter. Untuk menanyakan dokumen Anda, tambahkan query_document fungsi dalam kode berikut ke contoh kode sebelumnya. Kemudian, sertakan question variabel dan baris yang memanggil query_document fungsi ke fungsi sebelumnyamain.

   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

   Kode contoh berikut menampilkan dokumen dan kotak di sekitar item yang terdeteksi.

   Dalam kode berikut, ganti nilai bucket dan photo dengan nama bucket dan dokumen Amazon S3 yang Anda gunakan pada langkah 2. Ganti nilai region dengan wilayah yang terkait dengan akun Anda. Ganti nilai credentials dengan nama profil pengembang Anda.

   // 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

   Contoh berikut menampilkan teks yang terdeteksi dan hubungannya dalam daftar.

   Ganti nilai bucket dan document dengan nama bucket Amazon S3 dan gambar dokumen yang Anda gunakan pada langkah 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. Jalankan contohnya. Contoh Python dan Java menampilkan gambar dokumen dengan kotak pembatas berwarna berikut:

   • Merah - Objek Blok KUNCI

   • Hijau — Objek blok NILAI

   • Biru - objek Blok TABEL

   • Kuning - Objek Blok SEL

   Elemen seleksi yang dipilih diisi dengan warna biru.

   Contoh AWS CLI hanya menampilkan output JSON untuk AnalyzeDocument operasi.