Amazon Textract でドキュメントテキストの検出

ドキュメント内のテキストを検出するには (API)

1. まだ実行していない場合:

   1. を使用して IAM ユーザーを作成または更新するAmazonTextractFullAccessそしてAmazonS3ReadOnlyAccessアクセス許可。詳細については、「ステップ 1: AWS アカウントを設定して IAM ユーザーの作成」を参照してください。

   2. AWS CLI と AWS SDK をインストールして設定します。詳細については、「ステップ 2: のセットアップAWS CLIそしてAWSSDK」を参照してください。

2. ドキュメントを S3 バケットにアップロードします。

   手順については、以下を参照してください。Amazon S3 へのオブジェクトのアップロードのAmazon Simple Storage Service ユーザーガイド。

3. 以下の例を使用して、DetectDocumentText オペレーションを呼び出します。

   Java

   次のサンプルコードは、検出されたテキストの行を囲むドキュメントとボックスを表示します。

   関数内でmainで、の値を置き換えます。bucketそしてdocumentは、ステップ 2 で使用した Amazon S3 バケット名とドキュメント名を使用します。

   //Calls DetectDocumentText.
   //Loads document from S3 bucket. Displays the document and bounding boxes around detected lines/words 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.client.builder.AwsClientBuilder.EndpointConfiguration;
   import com.amazonaws.services.textract.AmazonTextract;
   import com.amazonaws.services.textract.AmazonTextractClientBuilder;
   import com.amazonaws.services.textract.model.Block;
   import com.amazonaws.services.textract.model.BoundingBox;
   import com.amazonaws.services.textract.model.DetectDocumentTextRequest;
   import com.amazonaws.services.textract.model.DetectDocumentTextResult;
   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;
   
   public class DocumentText extends JPanel {
   
       private static final long serialVersionUID = 1L;
   
       BufferedImage image;
       DetectDocumentTextResult result;
   
       public DocumentText(DetectDocumentTextResult 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 polygons around lines of detected text.
           List<Block> blocks = result.getBlocks();
           for (Block block : blocks) {
               DisplayBlockInfo(block);
               if ((block.getBlockType()).equals("LINE")) {
                   ShowPolygon(height, width, block.getGeometry().getPolygon(), g2d);
                   /*
                     ShowBoundingBox(height, width, block.getGeometry().getBoundingBox(), g2d);
                    */
               } else { // its a word, so just show vertical lines.
                   ShowPolygonVerticals(height, width, block.getGeometry().getPolygon(), g2d);
               }
           }
       }
   
       // Show bounding box at supplied location.
       private void ShowBoundingBox(int imageHeight, int imageWidth, BoundingBox box, Graphics2D g2d) {
   
           float left = imageWidth * box.getLeft();
           float top = imageHeight * box.getTop();
   
           // Display bounding box.
           g2d.setColor(new Color(0, 212, 0));
           g2d.drawRect(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);
       }
   
       // Draws only the vertical lines in the supplied polygon.
       private void ShowPolygonVerticals(int imageHeight, int imageWidth, List<Point> points, Graphics2D g2d) {
   
           g2d.setColor(new Color(0, 212, 0));
           Object[] parry = points.toArray();
           g2d.setStroke(new BasicStroke(2));
   
           g2d.drawLine(Math.round(((Point) parry[0]).getX() * imageWidth),
                   Math.round(((Point) parry[0]).getY() * imageHeight), Math.round(((Point) parry[3]).getX() * imageWidth),
                   Math.round(((Point) parry[3]).getY() * imageHeight));
   
           g2d.setColor(new Color(255, 0, 0));
           g2d.drawLine(Math.round(((Point) parry[1]).getX() * imageWidth),
                   Math.round(((Point) parry[1]).getY() * imageHeight), Math.round(((Point) parry[2]).getX() * imageWidth),
                   Math.round(((Point) parry[2]).getY() * imageHeight));
   
       }
       //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.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 DetectDocumentText
           EndpointConfiguration endpoint = new EndpointConfiguration(
                   "https://textract.us-east-1.amazonaws.com", "us-east-1");
           AmazonTextract client = AmazonTextractClientBuilder.standard()
                   .withEndpointConfiguration(endpoint).build();
   
   
           DetectDocumentTextRequest request = new DetectDocumentTextRequest()
               .withDocument(new Document().withS3Object(new S3Object().withName(document).withBucket(bucket)));
   
           DetectDocumentTextResult result = client.detectDocumentText(request);
           
           // Create frame and panel.
           JFrame frame = new JFrame("RotateImage");
           frame.setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE);
           DocumentText panel = new DocumentText(result, image);
           panel.setPreferredSize(new Dimension(image.getWidth() , image.getHeight() ));
           frame.setContentPane(panel);
           frame.pack();
           frame.setVisible(true);
   
       }
   }
   

   AWS CLI

   この AWS CLI コマンドでは、detect-document-text CLI オペレーションの JSON 出力を表示します。

   の値を置換するBucketそしてNameステップ 2 で使用した Amazon S3 バケット名とドキュメント名を指定します。

   aws textract detect-document-text \
    --document '{"S3Object":{"Bucket":"bucket","Name":"document"}}'

   Python

   次のコード例では、テキスト行周辺のドキュメントとボックスを表示します。

   関数内でmainで、の値を置き換えます。bucketそしてdocumentステップ 2 で使用した Amazon S3 バケット名とドキュメント名を指定します。

   #Detects 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 psutil
   import time
   
   import math
   from PIL import Image, ImageDraw, ImageFont
   
   
   # 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("        ColumnSpan: " + str(block['ColumnSpan']))
           print("        RowSpan: " + str(block['RowSpan']))    
       
       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 'Page' in block:
           print('Page: ' + block['Page'])
       print()
   
   def process_text_detection(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)
   
      
       # Detect text in the document
       
       client = boto3.client('textract')
       #process using image bytes                      
       #image_binary = stream.getvalue()
       #response = client.detect_document_text(Document={'Bytes': image_binary})
   
       #process using S3 object
       response = client.detect_document_text(
           Document={'S3Object': {'Bucket': bucket, 'Name': document}})
   
       #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:
               print('Type: ' + block['BlockType'])
               if block['BlockType'] != 'PAGE':
                   print('Detected: ' + block['Text'])
                   print('Confidence: ' + "{:.2f}".format(block['Confidence']) + "%")
   
               print('Id: {}'.format(block['Id']))
               if 'Relationships' in block:
                   print('Relationships: {}'.format(block['Relationships']))
               print('Bounding Box: {}'.format(block['Geometry']['BoundingBox']))
               print('Polygon: {}'.format(block['Geometry']['Polygon']))
               print()
               draw=ImageDraw.Draw(image)
               # Draw WORD - Green -  start of word, red - end of word
               if block['BlockType'] == "WORD":
                   draw.line([(width * block['Geometry']['Polygon'][0]['X'],
                   height * block['Geometry']['Polygon'][0]['Y']),
                   (width * block['Geometry']['Polygon'][3]['X'],
                   height * block['Geometry']['Polygon'][3]['Y'])],fill='green',
                   width=2)
               
                   draw.line([(width * block['Geometry']['Polygon'][1]['X'],
                   height * block['Geometry']['Polygon'][1]['Y']),
                   (width * block['Geometry']['Polygon'][2]['X'],
                   height * block['Geometry']['Polygon'][2]['Y'])],
                   fill='red',
                   width=2)    
   
                    
               # Draw box around entire LINE  
               if block['BlockType'] == "LINE":
                   points=[]
   
                   for polygon in block['Geometry']['Polygon']:
                       points.append((width * polygon['X'], height * polygon['Y']))
   
                   draw.polygon((points), outline='black')    
     
                   # Uncomment to draw bounding box
                   #box=block['Geometry']['BoundingBox']                    
                   #left = width * box['Left']
                   #top = height * box['Top']           
                   #draw.rectangle([left,top, left + (width * box['Width']), top +(height * box['Height'])],outline='black') 
   
   
       # Display the image
       image.show()
       # display image for 10 seconds
   
       
       return len(blocks)
   
   def main():
   
       bucket = ''
       document = ''
       block_count=process_text_detection(bucket,document)
       print("Blocks detected: " + str(block_count))
       
   if __name__ == "__main__":
       main()
   
   

   Node.js

   次の Node.js サンプルコードは、検出されたテキスト行の周囲のドキュメントとボックスを表示し、コードの実行元のディレクトリに結果のイメージを出力します。これは、image-sizeそしてimagesパッケージ。

   関数内でmainで、の値を置き換えます。bucketそしてdocumentステップ 2 で使用した Amazon S3 バケット名とドキュメント名を指定します。の値を置換するregionConfigアカウントが属しているリージョンの名前を指定します。

   async function main(){
   
   // Import AWS
   const AWS = require("aws-sdk")
   // Use Image-Size to get 
   const sizeOf = require('image-size');
   // Image tool to draw buffers
   const images = require("images");
   
   // Create a canvas and get the context
   const { createCanvas } = require('canvas')
   const canvas = createCanvas(200, 200)
   const ctx = canvas.getContext('2d')
   
   // Set variables
   const bucket = 'bucket-name' // the s3 bucket name
   const photo  = 'image-name' // the name of file
   const regionConfig = 'region'
   
   // Set region if needed
   AWS.config.update({region:regionConfig});
   
   // Connect to Textract
   const client = new AWS.Textract();
   // Connect to S3 to display image
   const s3 = new AWS.S3();
   
   // Define paramaters
   const params = {
     Document: {
       S3Object: {
         Bucket: bucket,
         Name: photo
       },
     },
   }
   
   // Function to display image
   async function getImage(){
     const imageData =  s3.getObject(
       {
           Bucket: bucket,
           Key: photo
         }
       
     ).promise();
     return imageData;
   }
   
   // get image
   var imageData = await getImage()
   
   // Get the height, width of the image
   const dimensions = sizeOf(imageData.Body)
   const width = dimensions.width
   const height = dimensions.height
   console.log(imageData.Body)
   console.log(width, height)
   
   canvas.width = width;
   canvas.height = height;
   
   try{
     // Call API and log response
     const res = await client.detectDocumentText(params).promise();
     var image = images(imageData.Body).size(width, height)
     //console.log the type of block, text, text type, and confidence
     res.Blocks.forEach(block => {
       console.log(`Block Type: ${block.BlockType}`),
       console.log(`Text: ${block.Text}`)
       console.log(`TextType: ${block.TextType}`)
       console.log(`Confidence: ${block.Confidence}`)
   
       // Draw box around detected text using polygons
       ctx.strokeStyle = 'rgba(0,0,0,0.5)';
       ctx.beginPath();
       block.Geometry.Polygon.forEach(({X, Y}) =>
       ctx.lineTo(width * X - 10, height * Y - 10)
       );
       ctx.closePath();
       ctx.stroke();
       console.log("-----")
     }) 
   
     // render image
     var buffer = canvas.toBuffer("image/png");
     image.draw(images(buffer), 10, 10)
     image.save("output-image.jpg");
     
   } catch (err){
   console.error(err);}
   
   }
   
   main()

4. 例を実行します。Python および Java の例には、ドキュメントイメージが表示されます。検出されたテキストの各行をブラックボックスで囲みます。緑色の縦線は、検出された単語の先頭です。赤い縦線は、検出された単語の終わりです。-AWS CLI例では、の JSON 出力のみを表示します。DetectDocumentTextオペレーション.