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Detectando texto do documento com o Amazon Textract
Para detectar texto em um documento, você usa oDetectDocumentTexte passe um arquivo de documento como entrada.DetectDocumentText
retorna uma estrutura JSON que contém linhas e palavras do texto detectado, a localização do texto no documento e as relações entre o texto detectado. Para obter mais informações, consulte Detectar texto.
Você pode fornecer um documento de entrada como uma matriz de bytes de imagem (bytes de imagem codificados em base64) ou um objeto do Amazon S3. Neste procedimento, você carrega um arquivo de imagem no bucket do S3 e especifica o nome do arquivo.
Para detectar texto em um documento (API do)
Se ainda não tiver feito isso:
Criar ou atualizar um usuário do IAM com
AmazonTextractFullAccess
eAmazonS3ReadOnlyAccess
permissões. Para obter mais informações, consulte Etapa 1: Configurar uma conta da AWS e criar um usuário do IAM.Instale e configure a AWS CLI e os SDKs da AWS. Para obter mais informações, consulte Etapa 2: Configurar aAWS CLIeAWSSDKs da.
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Carregue um documento para o bucket do S3.
Para obter instruções, consulteCarregar objetos no Amazon S3noGuia do usuário do Amazon Simple Storage Service.
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Use os exemplos a seguir para chamar a operação
DetectDocumentText
.- Java
-
O código de exemplo a seguir exibe o documento e as caixas em torno de linhas de texto detectado.
Na função
main
, substitua os valores debucket
edocument
Com os nomes do bucket do Amazon S3 e do documento usado na etapa 2.//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
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Esse comando da AWS CLI exibe a saída JSON da operação da CLI
detect-document-text
.Substitua os valores de
Bucket
eName
Com os nomes do bucket do Amazon S3 e do documento usado na etapa 2.aws textract detect-document-text \ --document '{"S3Object":{"Bucket":"
bucket
","Name":"document
"}}' - Python
-
O código de exemplo a seguir exibe o documento e as caixas ao redor de linhas de texto detectadas.
Na função
main
, substitua os valores debucket
edocument
Com os nomes do bucket do Amazon S3 e do documento usado na etapa 2.#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
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O código de exemplo Node.js a seguir exibe o documento e as caixas em torno de linhas de texto detectadas, gerando uma imagem dos resultados para o diretório do qual você executa o código. Ele faz uso do
image-size
eimages
Pacotes.Na função
main
, substitua os valores debucket
edocument
Com os nomes do bucket do Amazon S3 e do documento usado na etapa 2. Substitua o valor deregionConfig
com o nome da região em que sua conta está.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()
Execute o exemplo. Os exemplos Python e Java exibem a imagem do documento. Uma caixa preta envolve cada linha do texto detectado. Uma linha vertical verde é o início de uma palavra detectada. Uma linha vertical vermelha é o fim de uma palavra detectada. OAWS CLIO exemplo da exibe somente a saída JSON do
DetectDocumentText
operação.