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# Traitement de documents avec des opérations synchrone
Amazon Textract peut détecter et analyser du texte dans des documents d'une seule page fournis sous forme d'images au format JPEG, PNG, PDF et TIFF. Les opérations sont synchrones et renvoient les résultats en quasi temps réel. Pour plus d'informations sur les documents, consultez [Objets de réponse Détection de texte et analyse de documents](how-it-works-document-layout.md).
Cette section explique comment utiliser Amazon Textract pour détecter et analyser du texte dans un document d'une seule page de manière synchrone. Pour détecter et analyser du texte dans des documents multipages ou pour détecter des documents JPEG et PNG de manière asynchrone, reportez-vous à la section[Traitement de documents avec des opérations asynchrones](async.md).
Vous pouvez utiliser les opérations synchrone Amazon Textract aux fins suivantes :
+ Détection de texte : vous pouvez détecter des lignes et des mots sur une image de document d'une seule page à l'aide de la commande[DetectDocumentText](API_DetectDocumentText.md). Pour plus d'informations, consultez [Détection de texte](how-it-works-detecting.md).
+ Analyse de texte : vous pouvez identifier les relations entre le texte détecté sur un document d'une seule page à l'aide de la[AnalyzeDocument](API_AnalyzeDocument.md). Pour plus d'informations, consultez [Analyse des documents](how-it-works-analyzing.md).
+ Analyse des factures et des reçus : vous pouvez identifier les relations financières entre le texte détecté sur une facture ou un reçu d'une seule page à l'aide de l'opération AnalyzeExpense. Pour de plus amples informations, veuillez consulter[Analyse des factures et des reçus](invoices-receipts.md)
+ Analyse des documents d'identité : vous pouvez analyser les documents d'identité émis par le gouvernement américain et extraire des informations ainsi que des types d'informations courants trouvés sur les documents d'identité. Pour plus d'informations, consultez la section .[Analyse des documents d'identité](how-it-works-identity.md).
**Topics**
+ [Appel d'opérations synchrone Amazon Textract](sync-calling.md)
+ [Détection du texte de document avec Amazon Textract](detecting-document-text.md)
+ [Analyse du texte du document avec Amazon Textract](analyzing-document-text.md)
+ [Analyse des factures et des reçus avec Amazon Textract](analyzing-document-expense.md)
+ [Analyse de la documentation d'identité avec Amazon Textract](analyzing-document-identity.md)
# Appel d'opérations synchrone Amazon Textract
Les opérations Amazon Textract traitent les images de documents stockées sur un système de fichiers local ou les images de documents stockées dans un compartiment Amazon S3. Vous spécifiez où se trouve le document en entrée à l'aide de la commande[Document](API_Document.md)paramètre d'entrée. L'image du document peut être au format PNG, JPEG, PDF ou TIFF. Les résultats des opérations synchrone sont renvoyés immédiatement et ne sont pas stockés pour récupération.
Pour un exemple complet, consultez[Détection du texte de document avec Amazon Textract](detecting-document-text.md).
## Requête
La section suivante décrit comment les demandes fonctionnent dans Amazon Textract.
### Documents transmis sous forme d'octets d'image
Vous pouvez transmettre une image de document à une opération Amazon Textract en transmettant l'image sous la forme d'un tableau d'octets codé en base64. Par exemple, une image de document chargée à partir d'un système de fichiers local. Votre code n'a peut-être pas besoin d'encoder des octets de fichier de document si vous utilisez unAWSSDK pour appeler les opérations de l'API Amazon Textract.
Les octets de l'image sont spécifiés dans le`Bytes`du domaine`Document`paramètre d'entrée. L'exemple suivant illustre le JSON en entrée pour une opération Amazon Textract qui transmet les octets d'image dans la zone`Bytes`paramètre d'entrée.
```
{
"Document": {
"Bytes": "/9j/4AAQSk....."
}
}
```
**Note**
Si vous utilisez leAWS CLI, vous ne pouvez pas transmettre d'octets d'image aux opérations Amazon Textract. Vous devez plutôt référencer une image stockée dans un compartiment Amazon S3.
Le code Java suivant montre comment charger une image à partir d'un système de fichiers local et comment appeler une opération Amazon Textract.
```
String document="input.png";
ByteBuffer imageBytes;
try (InputStream inputStream = new FileInputStream(new File(document))) {
imageBytes = ByteBuffer.wrap(IOUtils.toByteArray(inputStream));
}
AmazonTextract client = AmazonTextractClientBuilder.defaultClient();
DetectDocumentTextRequest request = new DetectDocumentTextRequest()
.withDocument(new Document()
.withBytes(imageBytes));
DetectDocumentTextResult result = client.detectDocumentText(request);
```
### Documents stockés dans un compartiment Amazon S3
Amazon Textract peut analyser les images de documents stockées dans un compartiment Amazon S3. Vous spécifiez le compartiment et le nom de fichier à l'aide de[S3Object](API_S3Object.md)du domaine`Document`paramètre d'entrée. L'exemple suivant illustre le JSON en entrée pour une opération Amazon Textract qui traite un document stocké dans un compartiment Amazon S3.
```
{
"Document": {
"S3Object": {
"Bucket": "bucket",
"Name": "input.png"
}
}
}
```
L'exemple suivant montre comment appeler une opération Amazon Textract à l'aide d'une image stockée dans un compartiment Amazon S3.
```
String document="input.png";
String bucket="bucket";
AmazonTextract client = AmazonTextractClientBuilder.defaultClient();
DetectDocumentTextRequest request = new DetectDocumentTextRequest()
.withDocument(new Document()
.withS3Object(new S3Object()
.withName(document)
.withBucket(bucket)));
DetectDocumentTextResult result = client.detectDocumentText(request);
```
## Réponse
L'exemple suivant est la réponse JSON d'un appel à`DetectDocumentText`. Pour plus d'informations, consultez [Détection de texte](how-it-works-detecting.md).
```
{
{
"DocumentMetadata": {
"Pages": 1
},
"Blocks": [
{
"BlockType": "PAGE",
"Geometry": {
"BoundingBox": {
"Width": 0.9995205998420715,
"Height": 1.0,
"Left": 0.0,
"Top": 0.0
},
"Polygon": [
{
"X": 0.0,
"Y": 0.0
},
{
"X": 0.9995205998420715,
"Y": 2.297314024515845E-16
},
{
"X": 0.9995205998420715,
"Y": 1.0
},
{
"X": 0.0,
"Y": 1.0
}
]
},
"Id": "ca4b9171-7109-4adb-a811-e09bbe4834dd",
"Relationships": [
{
"Type": "CHILD",
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]
}
]
},
{
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"Text": "Employment Application",
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},
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},
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},
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},
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}
]
},
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{
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]
}
]
},
{
"BlockType": "LINE",
"Confidence": 99.91246795654297,
"Text": "Application Information",
"Geometry": {
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]
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{
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]
}
]
},
{
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"Text": "Full Name: Jane Doe",
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]
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]
},
{
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]
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]
},
{
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},
{
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"Geometry": {
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]
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]
},
{
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}
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},
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"Relationships": [
{
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]
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]
},
{
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"Text": "1/15/2009",
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"date": "Mon, 09 Nov 2020 23:54:38 GMT"
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}
}
```
# Détection du texte de document avec Amazon Textract
Pour détecter du texte dans un document, vous utilisez le[DetectDocumentText](API_DetectDocumentText.md)et transmettez un fichier de document en entrée.`DetectDocumentText`renvoie une structure JSON contenant des lignes et des mots de texte détecté, l'emplacement du texte dans le document et les relations entre le texte détecté. Pour plus d'informations, consultez [Détection de texte](how-it-works-detecting.md).
Vous pouvez fournir un document d'entrée sous la forme d'un tableau d'octets d'image (octets d'image encodés en base64) ou en tant qu'objet Amazon S3. Dans cette procédure, vous chargez un fichier image dans votre compartiment S3 et spécifiez le nom du fichier.
**Pour détecter du texte dans un document (API)**
1. Si vous ne l'avez pas déjà fait :
1. Créer ou mettre à jour un utilisateur IAM avec`AmazonTextractFullAccess`et`AmazonS3ReadOnlyAccess`Autorisations. Pour plus d'informations, consultez [Étape 1 : Configuration d'un compte AWS et création d'un utilisateur IAM](setting-up.md#setting-up-iam).
1. Installez et configurez l'AWS CLI et les kits SDK AWS. Pour plus d'informations, consultez [Étape 2 : Configuration de l'AWS CLIetAWSKits SDK](setup-awscli-sdk.md).
1. Chargez un document dans votre compartiment S3.
Pour obtenir des instructions, consultez[Chargement d'objets dans Amazon S3](https://docs.aws.amazon.com/AmazonS3/latest/user-guide/UploadingObjectsintoAmazonS3.html)dans le*Manuel de l'utilisateur Amazon Simple Storage Service*.
1. Utilisez les exemples suivants pour appeler l'opération `DetectDocumentText`.
------
#### [ Java ]
L'exemple de code suivant affiche le document et les zones autour des lignes de texte détecté.
Dans la fonction`main`, remplacez les valeurs de`bucket`et`document`avec les noms du compartiment Amazon S3 et du document utilisés à l'étape 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 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 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 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 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 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 ]
Cette commande de l'AWS CLI affiche la sortie JSON pour l'opération `detect-document-text` de l'interface de ligne de commande (CLI).
Remplacez les valeurs de`Bucket`et`Name`avec les noms du compartiment Amazon S3 et du document utilisés à l'étape 2.
```
aws textract detect-document-text \
--document '{"S3Object":{"Bucket":"bucket","Name":"document"}}'
```
------
#### [ Python ]
L'exemple de code suivant affiche le document et les zones autour des lignes de texte détectées.
Dans la fonction`main`, remplacez les valeurs de`bucket`et`document`avec les noms du compartiment Amazon S3 et du document utilisés à l'étape 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 ]
L'exemple de code Node.js suivant affiche le document et les cases autour des lignes de texte détectées, affichant une image des résultats dans le répertoire à partir de lequel vous exécutez le code. Il utilise le`image-size`et`images`paquets.
Dans la fonction`main`, remplacez les valeurs de`bucket`et`document`avec les noms du compartiment Amazon S3 et du document utilisés à l'étape 2. Remplacez la valeur de`regionConfig`avec le nom de la région dans laquelle se trouve votre compte.
```
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()
```
------
1. Exécutez l'exemple. Les exemples Python et Java affichent l'image du document. Une boîte noire entoure chaque ligne de texte détectée. Une ligne verticale verte est le début d'un mot détecté. Une ligne verticale rouge est la fin d'un mot détecté. LeAWS CLIl'exemple de l'affiche uniquement la sortie JSON pour le`DetectDocumentText`.
# Analyse du texte du document avec Amazon Textract
Pour analyser du texte dans un document, vous utilisez le[AnalyzeDocument](API_AnalyzeDocument.md)et transmettez un fichier de document en entrée.`AnalyzeDocument`renvoie une structure JSON qui contient le texte analysé. Pour plus d'informations, consultez [Analyse des documents](how-it-works-analyzing.md).
Vous pouvez fournir un document d'entrée sous la forme d'un tableau d'octets d'image (octets d'image encodés en base64) ou en tant qu'objet Amazon S3. Dans cette procédure, vous chargez un fichier image dans votre compartiment S3 et spécifiez le nom du fichier.
**Pour analyser du texte dans un document (API)**
1. Si vous ne l'avez pas déjà fait :
1. Créer ou mettre à jour un utilisateur IAM avec`AmazonTextractFullAccess`et`AmazonS3ReadOnlyAccess`Autorisations. Pour plus d'informations, consultez [Étape 1 : Configuration d'un compte AWS et création d'un utilisateur IAM](setting-up.md#setting-up-iam).
1. Installez et configurez l'AWS CLI et les kits SDK AWS. Pour plus d'informations, consultez [Étape 2 : Configuration de l'AWS CLIetAWSKits SDK](setup-awscli-sdk.md).
1. Chargez une image qui contient un document dans votre compartiment S3.
Pour obtenir des instructions, consultez[Chargement d'objets dans Amazon S3](https://docs.aws.amazon.com/AmazonS3/latest/user-guide/UploadingObjectsintoAmazonS3.html)dans le*Manuel de l'utilisateur Amazon Simple Storage Service*.
1. Utilisez les exemples suivants pour appeler l'opération `AnalyzeDocument`.
------
#### [ Java ]
L'exemple de code suivant affiche le document et les boîtes autour des éléments détectés.
Dans la fonction`main`, remplacez les valeurs de`bucket`et`document`avec les noms du compartiment Amazon S3 et de l'image de document utilisés à l'étape 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 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 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 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 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 ]
Cette commande de l'AWS CLI affiche la sortie JSON pour l'opération `detect-document-text` de l'interface de ligne de commande (CLI).
Remplacez les valeurs de`Bucket`et`Name`avec les noms du compartiment Amazon S3 et du document utilisés à l'étape 2.
```
aws textract analyze-document \
--document '{"S3Object":{"Bucket":"bucket","Name":"document"}}' \
--feature-types '["TABLES","FORMS"]'
```
------
#### [ Python ]
L'exemple de code suivant affiche le document et les boîtes autour des éléments détectés.
Dans la fonction`main`, remplacez les valeurs de`bucket`et`document`avec les noms du compartiment Amazon S3 et du document utilisés à l'étape 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 ]
L'exemple de code suivant affiche le document et les boîtes autour des éléments détectés.
Dans le code ci-dessous, remplacez les valeurs de`bucket`et`photo`avec les noms du compartiment Amazon S3 et du document utilisés à l'étape 2. Remplacez la valeur de`region`avec la région associée à votre compte.
```
// 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()
```
------
1. Exécutez l'exemple. Les exemples Python et Java affichent l'image du document avec les zones de sélection colorées suivantes :
+ Red — Objets KEY Block
+ Vert — objets Value Block
+ Blue — Objets BLOC TABLE
+ Yellow — objets CELL Block
Les éléments de sélection sélectionnés sont remplis de bleu.
LeAWS CLIl'exemple de l'affiche uniquement la sortie JSON pour le`AnalyzeDocument`.
# Analyse des factures et des reçus avec Amazon Textract
Pour analyser les documents de facture et de réception, vous utilisez l'API AnalyzeExpense et transmettez un fichier de document en entrée.`AnalyzeExpense`est une opération synchrone qui renvoie une structure JSON contenant le texte analysé. Pour plus d'informations, consultez [Analyse des factures et des reçus](invoices-receipts.md).
Pour analyser les factures et les réceptions de manière asynchrone, utilisez`StartExpenseAnalysis`pour commencer à traiter un fichier de document d'entrée et utiliser`GetExpenseAnalysis`pour obtenir les résultats.
Vous pouvez fournir un document d'entrée sous la forme d'un tableau d'octets d'image (octets d'image encodés en base64) ou en tant qu'objet Amazon S3. Dans cette procédure, vous chargez un fichier image dans votre compartiment S3 et spécifiez le nom du fichier.
**Pour analyser une facture ou un reçu (API)**
1. Si vous ne l'avez pas déjà fait :
1. Créer ou mettre à jour un utilisateur IAM avec`AmazonTextractFullAccess`et`AmazonS3ReadOnlyAccess`Autorisations. Pour plus d'informations, consultez [Étape 1 : Configuration d'un compte AWS et création d'un utilisateur IAM](setting-up.md#setting-up-iam).
1. Installez et configurez l'AWS CLI et les kits SDK AWS. Pour plus d'informations, consultez [Étape 2 : Configuration de l'AWS CLIetAWSKits SDK](setup-awscli-sdk.md).
1. Chargez une image qui contient un document dans votre compartiment S3.
Pour obtenir des instructions, consultez[Chargement d'objets dans Amazon S3](https://docs.aws.amazon.com/AmazonS3/latest/user-guide/UploadingObjectsintoAmazonS3.html)dans le*Manuel de l'utilisateur Amazon Simple Storage Service*.
1. Utilisez les exemples suivants pour appeler l'opération `AnalyzeExpense`.
------
#### [ CLI ]
```
aws textract analyze-expense --document '{"S3Object": {"Bucket": "bucket name","Name":
"object name"}}'
```
------
#### [ Python ]
```
import boto3
import io
from PIL import Image, ImageDraw
def draw_bounding_box(key, val, width, height, draw):
# If a key is Geometry, draw the bounding box info in it
if "Geometry" in key:
# Draw bounding box information
box = val["BoundingBox"]
left = width * box['Left']
top = height * box['Top']
draw.rectangle([left, top, left + (width * box['Width']), top + (height * box['Height'])],
outline='black')
# Takes a field as an argument and prints out the detected labels and values
def print_labels_and_values(field):
# Only if labels are detected and returned
if "LabelDetection" in field:
print("Summary Label Detection - Confidence: {}".format(
str(field.get("LabelDetection")["Confidence"])) + ", "
+ "Summary Values: {}".format(str(field.get("LabelDetection")["Text"])))
print(field.get("LabelDetection")["Geometry"])
else:
print("Label Detection - No labels returned.")
if "ValueDetection" in field:
print("Summary Value Detection - Confidence: {}".format(
str(field.get("ValueDetection")["Confidence"])) + ", "
+ "Summary Values: {}".format(str(field.get("ValueDetection")["Text"])))
print(field.get("ValueDetection")["Geometry"])
else:
print("Value Detection - No values returned")
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()
# opening binary stream using an in-memory bytes buffer
stream = io.BytesIO(s3_response['Body'].read())
# loading stream into image
image = Image.open(stream)
# Detect text in the document
client = boto3.client('textract', region_name="us-east-1")
# process using S3 object
response = client.analyze_expense(
Document={'S3Object': {'Bucket': bucket, 'Name': document}})
# Set width and height to display image and draw bounding boxes
# Create drawing object
width, height = image.size
draw = ImageDraw.Draw(image)
for expense_doc in response["ExpenseDocuments"]:
for line_item_group in expense_doc["LineItemGroups"]:
for line_items in line_item_group["LineItems"]:
for expense_fields in line_items["LineItemExpenseFields"]:
print_labels_and_values(expense_fields)
print()
print("Summary:")
for summary_field in expense_doc["SummaryFields"]:
print_labels_and_values(summary_field)
print()
#For draw bounding boxes
for line_item_group in expense_doc["LineItemGroups"]:
for line_items in line_item_group["LineItems"]:
for expense_fields in line_items["LineItemExpenseFields"]:
for key, val in expense_fields["ValueDetection"].items():
if "Geometry" in key:
draw_bounding_box(key, val, width, height, draw)
for label in expense_doc["SummaryFields"]:
if "LabelDetection" in label:
for key, val in label["LabelDetection"].items():
draw_bounding_box(key, val, width, height, draw)
# Display the image
image.show()
def main():
bucket = 'Bucket-Name'
document = 'Document-Name'
process_text_detection(bucket, document)
if __name__ == "__main__":
main()
```
------
#### [ Java ]
```
package com.amazonaws.samples;
import java.awt.*;
import java.awt.image.BufferedImage;
import java.io.ByteArrayInputStream;
import java.io.IOException;
import java.util.List;
import java.util.concurrent.CompletableFuture;
import javax.imageio.ImageIO;
import javax.swing.*;
import software.amazon.awssdk.auth.credentials.AwsBasicCredentials;
import software.amazon.awssdk.auth.credentials.StaticCredentialsProvider;
import software.amazon.awssdk.core.ResponseBytes;
import software.amazon.awssdk.core.async.AsyncResponseTransformer;
import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.s3.*;
import software.amazon.awssdk.services.s3.model.GetObjectRequest;
import software.amazon.awssdk.services.s3.model.GetObjectResponse;
import software.amazon.awssdk.services.textract.TextractClient;
import software.amazon.awssdk.services.textract.model.AnalyzeExpenseRequest;
import software.amazon.awssdk.services.textract.model.AnalyzeExpenseResponse;
import software.amazon.awssdk.services.textract.model.BoundingBox;
import software.amazon.awssdk.services.textract.model.Document;
import software.amazon.awssdk.services.textract.model.ExpenseDocument;
import software.amazon.awssdk.services.textract.model.ExpenseField;
import software.amazon.awssdk.services.textract.model.LineItemFields;
import software.amazon.awssdk.services.textract.model.LineItemGroup;
import software.amazon.awssdk.services.textract.model.S3Object;
import software.amazon.awssdk.services.textract.model.Point;
/**
*
* Demo code to parse Textract AnalyzeExpense API
*
*/
public class TextractAnalyzeExpenseSample extends JPanel {
private static final long serialVersionUID = 1L;
BufferedImage image;
static AnalyzeExpenseResponse result;
public TextractAnalyzeExpenseSample(AnalyzeExpenseResponse documentResult, BufferedImage bufImage) throws Exception {
super();
result = documentResult; // Results of analyzeexpense summaryfields and lineitemgroups detection.
image = bufImage; // The image containing the document.
}
// Draws the image and text bounding box.
public void paintComponent(Graphics g) {
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 summaryfields and lineitemgroups and display boundedboxes around lines of detected label and value.
List expenseDocuments = result.expenseDocuments();
for (ExpenseDocument expenseDocument : expenseDocuments) {
if (expenseDocument.hasSummaryFields()) {
DisplayAnalyzeExpenseSummaryInfo(expenseDocument);
List summaryfields = expenseDocument.summaryFields();
for (ExpenseField summaryfield : summaryfields) {
if (summaryfield.valueDetection() != null) {
ShowBoundingBox(image.getHeight(this), image.getWidth(this),
summaryfield.valueDetection().geometry().boundingBox(), g2d, new Color(0, 0, 0));
}
if (summaryfield.labelDetection() != null) {
ShowBoundingBox(image.getHeight(this), image.getWidth(this),
summaryfield.labelDetection().geometry().boundingBox(), g2d, new Color(0, 0, 0));
}
}
}
if (expenseDocument.hasLineItemGroups()) {
DisplayAnalyzeExpenseLineItemGroupsInfo(expenseDocument);
List lineitemgroups = expenseDocument.lineItemGroups();
for (LineItemGroup lineitemgroup : lineitemgroups) {
if (lineitemgroup.hasLineItems()) {
List lineItems = lineitemgroup.lineItems();
for (LineItemFields lineitemfield : lineItems) {
if (lineitemfield.hasLineItemExpenseFields()) {
List expensefields = lineitemfield.lineItemExpenseFields();
for (ExpenseField expensefield : expensefields) {
if (expensefield.valueDetection() != null) {
ShowBoundingBox(image.getHeight(this), image.getWidth(this),
expensefield.valueDetection().geometry().boundingBox(), g2d,
new Color(0, 0, 0));
}
if (expensefield.labelDetection() != null) {
ShowBoundingBox(image.getHeight(this), image.getWidth(this),
expensefield.labelDetection().geometry().boundingBox(), g2d,
new Color(0, 0, 0));
}
}
}
}
}
}
}
}
}
// Show bounding box at supplied location.
private void ShowBoundingBox(float imageHeight, float imageWidth, BoundingBox box, Graphics2D g2d, Color color) {
float left = imageWidth * box.left();
float top = imageHeight * box.top();
// Display bounding box.
g2d.setColor(color);
g2d.drawRect(Math.round(left), Math.round(top), Math.round(imageWidth * box.width()),
Math.round(imageHeight * box.height()));
}
private void ShowSelectedElement(float imageHeight, float imageWidth, BoundingBox box, Graphics2D g2d,
Color color) {
float left = (float) imageWidth * (float) box.left();
float top = (float) imageHeight * (float) box.top();
System.out.println(left);
System.out.println(top);
// Display bounding box.
g2d.setColor(color);
g2d.fillRect(Math.round(left), Math.round(top), Math.round(imageWidth * box.width()),
Math.round(imageHeight * box.height()));
}
// Shows polygon at supplied location
private void ShowPolygon(int imageHeight, int imageWidth, List 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.x() * imageWidth)), Math.round(point.y() * imageHeight));
}
g2d.drawPolygon(polygon);
}
private void DisplayAnalyzeExpenseSummaryInfo(ExpenseDocument expensedocument) {
System.out.println(" ExpenseId : " + expensedocument.expenseIndex());
System.out.println(" Expense Summary information:");
if (expensedocument.hasSummaryFields()) {
List summaryfields = expensedocument.summaryFields();
for (ExpenseField summaryfield : summaryfields) {
System.out.println(" Page: " + summaryfield.pageNumber());
if (summaryfield.type() != null) {
System.out.println(" Expense Summary Field Type:" + summaryfield.type().text());
}
if (summaryfield.labelDetection() != null) {
System.out.println(" Expense Summary Field Label:" + summaryfield.labelDetection().text());
System.out.println(" Geometry");
System.out.println(" Bounding Box: "
+ summaryfield.labelDetection().geometry().boundingBox().toString());
System.out.println(
" Polygon: " + summaryfield.labelDetection().geometry().polygon().toString());
}
if (summaryfield.valueDetection() != null) {
System.out.println(" Expense Summary Field Value:" + summaryfield.valueDetection().text());
System.out.println(" Geometry");
System.out.println(" Bounding Box: "
+ summaryfield.valueDetection().geometry().boundingBox().toString());
System.out.println(
" Polygon: " + summaryfield.valueDetection().geometry().polygon().toString());
}
}
}
}
private void DisplayAnalyzeExpenseLineItemGroupsInfo(ExpenseDocument expensedocument) {
System.out.println(" ExpenseId : " + expensedocument.expenseIndex());
System.out.println(" Expense LineItemGroups information:");
if (expensedocument.hasLineItemGroups()) {
List lineitemgroups = expensedocument.lineItemGroups();
for (LineItemGroup lineitemgroup : lineitemgroups) {
System.out.println(" Expense LineItemGroupsIndexID :" + lineitemgroup.lineItemGroupIndex());
if (lineitemgroup.hasLineItems()) {
List lineItems = lineitemgroup.lineItems();
for (LineItemFields lineitemfield : lineItems) {
if (lineitemfield.hasLineItemExpenseFields()) {
List expensefields = lineitemfield.lineItemExpenseFields();
for (ExpenseField expensefield : expensefields) {
if (expensefield.type() != null) {
System.out.println(" Expense LineItem Field Type:" + expensefield.type().text());
}
if (expensefield.valueDetection() != null) {
System.out.println(
" Expense Summary Field Value:" + expensefield.valueDetection().text());
System.out.println(" Geometry");
System.out.println(" Bounding Box: "
+ expensefield.valueDetection().geometry().boundingBox().toString());
System.out.println(" Polygon: "
+ expensefield.valueDetection().geometry().polygon().toString());
}
if (expensefield.labelDetection() != null) {
System.out.println(
" Expense LineItem Field Label:" + expensefield.labelDetection().text());
System.out.println(" Geometry");
System.out.println(" Bounding Box: "
+ expensefield.labelDetection().geometry().boundingBox().toString());
System.out.println(" Polygon: "
+ expensefield.labelDetection().geometry().polygon().toString());
}
}
}
}
}
}
}
}
public static void main(String arg[]) throws Exception {
// Creates a default async client with credentials and AWS Region loaded from
// the
// environment
S3AsyncClient client = S3AsyncClient.builder().region(Region.US_EAST_1).build();
System.out.println("Creating the S3 Client");
// Start the call to Amazon S3, not blocking to wait for the result
CompletableFuture> responseFuture = client.getObject(
GetObjectRequest.builder().bucket("textractanalyzeexpense").key("input/sample-receipt.jpg").build(),
AsyncResponseTransformer.toBytes());
System.out.println("Successfully read the object");
// When future is complete (either successfully or in error), handle the
// response
CompletableFuture> operationCompleteFuture = responseFuture
.whenComplete((getObjectResponse, exception) -> {
if (getObjectResponse != null) {
// At this point, the file my-file.out has been created with the data
// from S3; let's just print the object version
// Convert this into Async call and remove the below block from here and put it
// outside
TextractClient textractclient = TextractClient.builder().region(Region.US_EAST_1).build();
AnalyzeExpenseRequest request = AnalyzeExpenseRequest.builder()
.document(
Document.builder().s3Object(S3Object.builder().name("YOURObjectName")
.bucket("YOURBucket").build()).build())
.build();
AnalyzeExpenseResponse result = textractclient.analyzeExpense(request);
System.out.print(result.toString());
ByteArrayInputStream bais = new ByteArrayInputStream(getObjectResponse.asByteArray());
try {
BufferedImage image = ImageIO.read(bais);
System.out.println("Successfully read the image");
JFrame frame = new JFrame("Expense Image");
frame.setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE);
TextractAnalyzeExpense panel = new TextractAnalyzeExpense(result, image);
panel.setPreferredSize(new Dimension(image.getWidth(), image.getHeight()));
frame.setContentPane(panel);
frame.pack();
frame.setVisible(true);
} catch (IOException e) {
throw new RuntimeException(e);
} catch (Exception e) {
// TODO Auto-generated catch block
e.printStackTrace();
}
} else {
// Handle the error
exception.printStackTrace();
}
});
// We could do other work while waiting for the AWS call to complete in
// the background, but we'll just wait for "whenComplete" to finish instead
operationCompleteFuture.join();
}
}
```
------
#### [ Node.Js ]
```
// Import required AWS SDK clients and commands for Node.js
import { AnalyzeExpenseCommand } 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 = 'bucket'
const photo = 'photo'
// Set params
const params = {
Document: {
S3Object: {
Bucket: bucket,
Name: photo
},
},
}
const process_text_detection = async () => {
try {
const aExpense = new AnalyzeExpenseCommand(params);
const response = await textractClient.send(aExpense);
//console.log(response)
response.ExpenseDocuments.forEach(doc => {
doc.LineItemGroups.forEach(items => {
items.LineItems.forEach(fields => {
fields.LineItemExpenseFields.forEach(expenseFields =>{
console.log(expenseFields)
})
}
)}
)
}
)
return response; // For unit tests.
} catch (err) {
console.log("Error", err);
}
}
process_text_detection()
```
------
1. Vous obtiendrez ainsi la sortie JSON pour`AnalyzeExpense`.
# Analyse de la documentation d'identité avec Amazon Textract
Pour analyser les documents d'identité, vous utilisez l'API AnalyzeID et transmettez un fichier de document en entrée.`AnalyzeID`renvoie une structure JSON qui contient le texte analysé. Pour plus d'informations, consultez [Analyse des documents d'identité](how-it-works-identity.md).
Vous pouvez fournir un document d'entrée sous la forme d'un tableau d'octets d'image (octets d'image encodés en base64) ou en tant qu'objet Amazon S3. Dans cette procédure, vous chargez un fichier image dans votre compartiment S3 et spécifiez le nom du fichier.
**Pour analyser un document d'identité (API)**
1. Si vous ne l'avez pas déjà fait :
1. Créer ou mettre à jour un utilisateur IAM avec`AmazonTextractFullAccess`et`AmazonS3ReadOnlyAccess`Autorisations. Pour plus d'informations, consultez [Étape 1 : Configuration d'un compte AWS et création d'un utilisateur IAM](setting-up.md#setting-up-iam).
1. Installez et configurez l'AWS CLI et les kits SDK AWS. Pour plus d'informations, consultez [Étape 2 : Configuration de l'AWS CLIetAWSKits SDK](setup-awscli-sdk.md).
1. Chargez une image qui contient un document dans votre compartiment S3.
Pour obtenir des instructions, consultez[Chargement d'objets dans Amazon S3](https://docs.aws.amazon.com/AmazonS3/latest/user-guide/UploadingObjectsintoAmazonS3.html)dans le*Manuel de l'utilisateur Amazon Simple Storage Service*.
1. Utilisez les exemples suivants pour appeler l'opération `AnalyzeID`.
------
#### [ CLI ]
L'exemple suivant prend en charge un fichier d'entrée à partir d'un compartiment S3 et exécute le`AnalyzeID`opération sur elle. Dans le code ci-dessous, remplacez la valeur de*seau*avec le nom de votre compartiment S3, la valeur de*fichier*avec le nom du fichier dans votre compartiment et la valeur de*région*avec le nom du`region`associé à votre compte.
```
aws textract analyze-id --document-pages '[{"S3Object":{"Bucket":"bucket","Name":"name"}}]' --region region
```
Vous pouvez également appeler l'API à l'avant et à l'arrière d'un permis de conduire en ajoutant un autre objet S3 à l'entrée.
```
aws textract analyze-id --document-pages '[{"S3Object":{"Bucket":"bucket","Name":"name front"}}, {"S3Object":{"Bucket":"bucket","Name":"name back"}}]' --region us-east-1
```
Si vous accédez à l'interface de ligne de commande sur un appareil Windows, utilisez des guillemets doubles au lieu de guillemets simples et échappez aux guillemets doubles internes par une barre oblique inverse (c'est-à-dire \$1) pour résoudre les erreurs d'analyseur que vous pourriez rencontrer. Pour un exemple, veuillez consulter ci-dessous :
```
aws textract analyze-id --document-pages "[{\"S3Object\":{\"Bucket\":\"bucket\",\"Name\":\"name\"}}]" --region region
```
------
#### [ Python ]
L'exemple suivant prend en charge un fichier d'entrée à partir d'un compartiment S3 et exécute le`AnalyzeID`en renvoyant les paires clé-valeur détectées. Dans le code ci-dessous, remplacez la valeur de*bucket\$1name*avec le nom de votre compartiment S3, la valeur de*file\$1name*avec le nom du fichier dans votre compartiment et la valeur de*région*avec le nom du`region`associé à votre compte.
```
import boto3
bucket_name = "bucket-name"
file_name = "file-name"
region = "region-name"
def analyze_id(region, bucket_name, file_name):
textract_client = boto3.client('textract', region_name=region)
response = textract_client.analyze_id(DocumentPages=[{"S3Object":{"Bucket":bucket_name,"Name":file_name}}])
for doc_fields in response['IdentityDocuments']:
for id_field in doc_fields['IdentityDocumentFields']:
for key, val in id_field.items():
if "Type" in str(key):
print("Type: " + str(val['Text']))
for key, val in id_field.items():
if "ValueDetection" in str(key):
print("Value Detection: " + str(val['Text']))
print()
analyze_id(region, bucket_name, file_name)
```
------
#### [ Java ]
L'exemple suivant prend en charge un fichier d'entrée à partir d'un compartiment S3 et exécute le`AnalyzeID`opération sur elle, renvoyant les données détectées. Dans la fonction principale, remplacez les valeurs de`s3bucket`et`sourceDoc`avec les noms du compartiment Amazon S3 et de l'image de document utilisés à l'étape 2.
```
/*
Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
SPDX-License-Identifier: Apache-2.0
*/
package com.amazonaws.samples;
import com.amazonaws.regions.Regions;
import com.amazonaws.services.textract.AmazonTextractClient;
import com.amazonaws.services.textract.AmazonTextractClientBuilder;
import com.amazonaws.services.textract.model.*;
import java.util.ArrayList;
import java.util.List;
public class AnalyzeIdentityDocument {
public static void main(String[] args) {
final String USAGE = "\n" +
"Usage:\n" +
" \n\n" +
"Where:\n" +
" s3bucket - the Amazon S3 bucket where the document is located. \n" +
" sourceDoc - the name of the document. \n";
if (args.length != 1) {
System.out.println(USAGE);
System.exit(1);
}
String s3bucket = "bucket-name"; //args[0];
String sourceDoc = "sourcedoc-name"; //args[1];
AmazonTextractClient textractClient = (AmazonTextractClient) AmazonTextractClientBuilder.standard()
.withRegion(Regions.US_EAST_1)
.build();
getDocDetails(textractClient, s3bucket, sourceDoc);
}
public static void getDocDetails(AmazonTextractClient textractClient, String s3bucket, String sourceDoc ) {
try {
S3Object s3 = new S3Object();
s3.setBucket(s3bucket);
s3.setName(sourceDoc);
com.amazonaws.services.textract.model.Document myDoc = new com.amazonaws.services.textract.model.Document();
myDoc.setS3Object(s3);
List list1 = new ArrayList();
list1.add(myDoc);
AnalyzeIDRequest idRequest = new AnalyzeIDRequest();
idRequest.setDocumentPages(list1);
AnalyzeIDResult result = textractClient.analyzeID(idRequest);
List docs = result.getIdentityDocuments();
for (IdentityDocument doc: docs) {
ListidFields = doc.getIdentityDocumentFields();
for (IdentityDocumentField field: idFields) {
System.out.println("Field type is "+ field.getType().getText());
System.out.println("Field value is "+ field.getValueDetection().getText());
}
}
} catch (Exception e) {
e.printStackTrace();
}
}
}
```
------
1. Vous obtiendrez ainsi la sortie JSON pour`AnalyzeID`.