Analyzing Invoices and Receipts with Amazon Textract

To analyze invoice and receipt documents, use the AnalyzeExpense API operations 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.

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)

If you haven't already:
1. Give a user the AmazonTextractFullAccess and AmazonS3ReadOnlyAccess permissions. For more information, see Step 1: Set Up an AWS Account and Create a User.
2. Install and configure the AWS CLI and the AWS SDKs. For more information, see Step 2: Set Up the AWS CLI and AWS SDKs.
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.

Use the following examples to call the AnalyzeExpense operation.

AWS CLI

This AWS CLI command displays the JSON output for the analyze-expense CLI operation.

Replace the values of Bucket and Name with the names of the Amazon S3 bucket and document that you used in step 2. Replace profile-name with the name of a profile that can assume the role and region with the region in which you want to run the code.


aws textract analyze-expense \
    --document '{"S3Object": {"Bucket": "bucket","Name": "object"}}' \
    --profile profile-name \
    --region region

Python

The following example code displays the document and boxes around detected items.

In the function main, replace the values of bucket and document with the names of the Amazon S3 bucket and document that you used in step 2. Replace profile-name with the name of a profile that can assume the role and region with the region in which you want to run the code.



                    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_expense_analysis(s3_connection, client, bucket, document):
        
    # Get the document from 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)

    # Analyze document
    # 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():
    session = boto3.Session(profile_name='profile-name')
    s3_connection = session.resource('s3')
    client = session.client('textract', region_name='region')
    bucket = 'bucket'
    document = 'document'
    process_expense_analysis(s3_connection, client, bucket, document)

if __name__ == "__main__":
    main()

Java

The following example code displays the document and boxes around detected items.

In the function main, replace the values of bucket and document with the names of the Amazon S3 bucket and document image that you used in step 2.



                    
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

The following example code displays the document and boxes around detected items.

In the function main, replace the values of bucket and photo with the names of the Amazon S3 bucket and document that you used in step 2. Replace profileName with the name of a profile that can assume the role and region with the region in which you want to run the code.



                    // Import required AWS SDK clients and commands for Node.js
import { AnalyzeExpenseCommand } from  "@aws-sdk/client-textract";
import  { TextractClient } from "@aws-sdk/client-textract";
import {fromIni} from '@aws-sdk/credential-providers';

// Set the AWS Region.
const REGION = "region-name"; //e.g. "us-east-1"
const profileName = "profile-name";
// Create SNS service object.
const textractClient = new TextractClient({region: REGION, 
  credentials: fromIni({profile: profileName,}), 
});

const bucket = 'bucket-name'
const photo = 'photo-name'

// 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()

This will provide you with the JSON output for the AnalyzeExpense operation.

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Document Conventions

Analyzing Document Text

Analyzing ID Documents