Analisando texto do documento com o Amazon Textract

Para analisar texto em um documento (API)

1. Se ainda não tiver feito isso:

   1. Criar ou atualizar um usuário do IAM comAmazonTextractFullAccesseAmazonS3ReadOnlyAccesspermissões. Para obter mais informações, consulte Etapa 1: Configurar uma conta da AWS e criar um usuário do IAM.

   2. Instale e configure a AWS CLI e os SDKs da AWS. Para obter mais informações, consulte Etapa 2: Configurar aAWS CLIeAWSSDKs da.

2. Carregue uma imagem que contenha um documento no bucket do S3.

   Para obter instruções, consulteCarregar objetos no Amazon S3noGuia 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 de itens detectados.

   Na funçãomain, substitua os valores debucketedocumentCom os nomes do bucket do Amazon S3 e da imagem usada na etapa 2.

   
   //Loads document from S3 bucket. Displays the document and polygon around detected lines of text.
   package com.amazonaws.samples;
   
   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.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 = "";
   
           AmazonS3 s3client = AmazonS3ClientBuilder.standard()
                   .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()
                   .withEndpointConfiguration(endpoint).build();
   
                   
           AnalyzeDocumentRequest request = new AnalyzeDocumentRequest()
                   .withFeatureTypes("TABLES","FORMS")
                    .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); 
   
       }
   }
   

   AWS CLI

   Esse comando da AWS CLI exibe a saída JSON da operação da CLI detect-document-text.

   Substitua os valores deBucketeNameCom os nomes do bucket do Amazon S3 e do documento usado na etapa 2.

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

   Python

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

   Na funçãomain, substitua os valores debucketedocumentCom os nomes do bucket do Amazon S3 e do documento usado na etapa 2.

   
   #Analyzes text in a document stored in an S3 bucket. Display polygon box around text and angled text 
   import boto3
   import io
   from io import BytesIO
   import sys
   
   import math
   from PIL import Image, ImageDraw, ImageFont
   
   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(bucket, document):
   
       #Get the document from S3
       s3_connection = boto3.resource('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
       client = boto3.client('textract')
       
       image_binary = stream.getvalue()
       response = client.analyze_document(Document={'Bytes': image_binary},
           FeatureTypes=["TABLES", "FORMS"])
   
       ### Alternatively, process using S3 object ###
       #response = client.analyze_document(
       #    Document={'S3Object': {'Bucket': bucket, 'Name': document}},
       #    FeatureTypes=["TABLES", "FORMS"])
   
       ### To use 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"])
   
       
       #Get the text blocks
       blocks=response['Blocks']
       width, height =image.size  
       draw = ImageDraw.Draw(image)  
       print ('Detected Document Text')
      
       # Create image showing bounding box/polygon the detected lines/text
       for block in blocks:
   
           DisplayBlockInformation(block)
                
           draw=ImageDraw.Draw(image)
           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')    
      
               #uncomment to draw polygon for all Blocks
               #points=[]
               #for polygon in block['Geometry']['Polygon']:
               #    points.append((width * polygon['X'], height * polygon['Y']))
               #draw.polygon((points), outline='blue')
               
       # Display the image
       image.show()
       return len(blocks)
   
   
   def main():
   
       bucket = ''
       document = ''
       block_count=process_text_analysis(bucket,document)
       print("Blocks detected: " + str(block_count))
       
   if __name__ == "__main__":
       main()
   

   Node.js

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

   No código abaixo, substitua os valores debucketephotoCom os nomes do bucket do Amazon S3 e do documento usado na etapa 2. Substitua o valor deregionCom a região associada à sua conta do.

   // Import required AWS SDK clients and commands for Node.js
   import { AnalyzeDocumentCommand } from  "@aws-sdk/client-textract";
   import  { TextractClient } from "@aws-sdk/client-textract";
   
   // Set the AWS Region.
   const REGION = "region"; //e.g. "us-east-1"
   // Create SNS service object.
   const textractClient = new TextractClient({ region: REGION });
   
   const bucket = 'buckets'
   const photo = 'photo'
   
   // Set params
   const params = {
       Document: {
         S3Object: {
           Bucket: bucket,
           Name: photo
         },
       },
       FeatureTypes: ['TABLES', 'FORMS'],
     }
   
   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()

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

   • Vermelho — Objetos KEY Block

   • Verde — VALUE Block objetos

   • Azul — Objetos TABLE Block

   • Amarelo — Objetos CELL Block

   Os elementos de seleção selecionados são preenchidos com azul.

   OAWS CLIO exemplo da exibe somente a saída JSON doAnalyzeDocumentoperação.