Menganalisis Teks Dokumen dengan Amazon Textract

Untuk menganalisis teks dalam dokumen, Anda menggunakanAnalyzeDocumentoperasi, dan lulus file dokumen sebagai masukan.AnalyzeDocumentmengembalikan struktur JSON yang berisi teks yang dianalisis. Untuk informasi selengkapnya, lihat Menganalisis Dokumen.

Anda dapat menyediakan dokumen input sebagai array bit citra (bit citra yang dikodekan base64), atau sebagai objek Amazon S3. Dalam prosedur ini, Anda mengunggah file citra ke bucket S3 Anda dan menentukan nama file.

Untuk menganalisis teks dalam dokumen (API)

Jika belum:
1. Buat atau perbarui pengguna IAM dengan izin AmazonTextractFullAccess dan AmazonS3ReadOnlyAccess. Untuk informasi selengkapnya, lihat Langkah 1: Siapkan Akun AWS dan Buat Pengguna IAM.
2. Instal dan konfigurasikan SDK AWS CLI dan AWS. Untuk informasi selengkapnya, lihat Langkah 2: MenyiapkanAWS CLIdanAWSSDK.
Unggah citra yang berisi dokumen ke bucket S3 Anda.

Untuk instruksi, lihatMengunggah Objek ke Amazon S3diPanduan Pengguna Amazon Simple Storage Service.

Gunakan contoh berikut untuk memanggil operasi AnalyzeDocument.

Java

Contoh kode berikut menampilkan dokumen dan kotak di sekitar item yang terdeteksi.

Fungsimain, ganti nilaibucketdandocumentdengan nama bucket Amazon S3 dan citra dokumen yang Anda gunakan pada langkah 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

Perintah AWS CLI ini menampilkan output JSON untuk operasi CLI detect-document-text.

Ganti nilaiBucketdanNamedengan nama bucket Amazon S3 dan dokumen yang Anda gunakan pada langkah 2.


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

Python

Contoh kode berikut menampilkan dokumen dan kotak di sekitar item yang terdeteksi.

Fungsimain, ganti nilaibucketdandocumentdengan nama bucket Amazon S3 dan dokumen yang Anda gunakan pada langkah 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

Contoh kode berikut menampilkan dokumen dan kotak di sekitar item yang terdeteksi.

Pada kode di bawah ini, ganti nilaibucketdanphotodengan nama bucket Amazon S3 dan dokumen yang Anda gunakan pada langkah 2. Ganti nilairegiondengan wilayah yang terkait dengan akun 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";

// 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()

Jalankan contoh. Contoh Python dan Java menampilkan gambar dokumen dengan kotak pembatas berwarna berikut:
- Merah - objek Blok KUNCI
- Hijau - NILAI Blok objek
- Biru - TABLE Blok benda
- Kuning - SELL Blok benda
Elemen seleksi yang dipilih diisi dengan warna biru.

ParameterAWS CLIcontoh hanya menampilkan output JSON untukAnalyzeDocumentoperasi.

Awas Javascript dinonaktifkan atau tidak tersedia di browser Anda.

Untuk menggunakan Dokumentasi AWS, Javascript harus diaktifkan. Lihat halaman Bantuan browser Anda untuk petunjuk.

Konvensi Dokumen

Mendeteksi teks

Menganalisis Dokumen Faktur dan Penerimaan