console.log("---") });
} catch (err) {
console.log("Error", err);
} }
const analyze_document_text = async () => { try {
const analyzeDoc = new AnalyzeDocumentCommand(params);
const response = await textractClient.send(analyzeDoc);
//console.log(response) displayBlockInfo(response)
return response; // For unit tests.
} catch (err) {
console.log("Error", err);
} }
analyze_document_text()
4. Run the example. The Python and Java examples display the document image with the following colored bounding boxes:
• Red – KEY Block objects
• Green – VALUE Block objects
• Blue – TABLE Block objects
• Yellow – CELL Block objects
Selection elements that are selected are filled with blue.
The AWS CLI example displays only the JSON output for the AnalyzeDocument operation.
Analyzing Invoices and Receipts with Amazon Textract
To analyze invoice and receipt documents, you use the AnalyzeExpense API, and pass a document file as input. AnalyzeExpense is a synchronous operation that returns a JSON structure that contains the analyzed text. For more information, see Analyzing Invoices and Receipts (p. 5).
To analyze invoice and receipts asynchronously, use StartExpenseAnalysis to start processing an input document file and use GetExpenseAnalysis to get the results.
You can provide an input document as an image byte array (base64-encoded image bytes), or as an Amazon S3 object. In this procedure, you upload an image file to your S3 bucket and specify the file name.
To analyze an invoice or receipt (API)
1. If you haven't already:a. Create or update an IAM user with AmazonTextractFullAccess and
AmazonS3ReadOnlyAccess permissions. For more information, see Step 1: Set Up an AWS Account and Create an IAM User (p. 31).
b. Install and configure the AWS CLI and the AWS SDKs. For more information, see Step 2: Set Up the AWS CLI and AWS SDKs (p. 31).
2. Upload an image that contains a document to your S3 bucket.
For instructions, see Uploading Objects into Amazon S3 in the Amazon Simple Storage Service User Guide.
3. Use the following examples to call the AnalyzeExpense operation.
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<ExpenseDocument> expenseDocuments = result.expenseDocuments();
for (ExpenseDocument expenseDocument : expenseDocuments) { if (expenseDocument.hasSummaryFields()) {
DisplayAnalyzeExpenseSummaryInfo(expenseDocument);
List<ExpenseField> 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<LineItemGroup> lineitemgroups = expenseDocument.lineItemGroups();
for (LineItemGroup lineitemgroup : lineitemgroups) { if (lineitemgroup.hasLineItems()) {
List<LineItemFields> lineItems = lineitemgroup.lineItems();
for (LineItemFields lineitemfield : lineItems) { if (lineitemfield.hasLineItemExpenseFields()) {
List<ExpenseField> 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<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.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<ExpenseField> 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<LineItemGroup> lineitemgroups = expensedocument.lineItemGroups();
for (LineItemGroup lineitemgroup : lineitemgroups) {
System.out.println(" Expense LineItemGroupsIndexID :" + lineitemgroup.lineItemGroupIndex());
if (lineitemgroup.hasLineItems()) {
List<LineItemFields> lineItems = lineitemgroup.lineItems();
for (LineItemFields lineitemfield : lineItems) { if (lineitemfield.hasLineItemExpenseFields()) {
List<ExpenseField> 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<ResponseBytes<GetObjectResponse>> 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<ResponseBytes<GetObjectResponse>> 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()
4. This will provide you with the JSON output for the AnalyzeExpense operation.