Detecting Document Text with Amazon Textract - Amazon Textract

Detecting Document Text with Amazon Textract

To detect text in a document, you use the DetectDocumentText operation, and pass a document file as input. DetectDocumentText returns a JSON structure that contains lines and words of detected text, the location of the text in the document, and the relationships between detected text. For more information, see Detecting Text.

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 detect text in a document (API)
  1. 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.

  2. Upload 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 DetectDocumentText operation.


    The following example code displays the document and boxes around lines of detected text.

    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 the value of credentialsProvider with the name of your developer profile.

    //Calls DetectDocumentText. //Loads document from S3 bucket. Displays the document and bounding boxes around detected lines/words of text. import java.awt.*; import java.awt.image.BufferedImage; import java.util.List; import javax.imageio.ImageIO; import javax.swing.*; import; import; import; import com.amazonaws.client.builder.AwsClientBuilder.EndpointConfiguration; import com.amazonaws.auth.profile.ProfileCredentialsProvider; import; import; import; import; import; import; import; import; import; import; public class DocumentText extends JPanel { private static final long serialVersionUID = 1L; BufferedImage image; DetectDocumentTextResult result; public DocumentText(DetectDocumentTextResult documentResult, BufferedImage bufImage) throws Exception { super(); result = documentResult; // Results of text detection. image = bufImage; // The image containing the document. } // Draws the image and text bounding box. public void paintComponent(Graphics g) { int height = image.getHeight(this); int width = image.getWidth(this); Graphics2D g2d = (Graphics2D) g; // Create a Java2D version of g. // Draw the image. g2d.drawImage(image, 0, 0, image.getWidth(this) , image.getHeight(this), this); // Iterate through blocks and display polygons around lines of detected text. List<Block> blocks = result.getBlocks(); for (Block block : blocks) { DisplayBlockInfo(block); if ((block.getBlockType()).equals("LINE")) { ShowPolygon(height, width, block.getGeometry().getPolygon(), g2d); /* ShowBoundingBox(height, width, block.getGeometry().getBoundingBox(), g2d); */ } else { // its a word, so just show vertical lines. ShowPolygonVerticals(height, width, block.getGeometry().getPolygon(), g2d); } } } // Show bounding box at supplied location. private void ShowBoundingBox(int imageHeight, int imageWidth, BoundingBox box, Graphics2D g2d) { float left = imageWidth * box.getLeft(); float top = imageHeight * box.getTop(); // Display bounding box. g2d.setColor(new Color(0, 212, 0)); g2d.drawRect(Math.round(left), Math.round(top), Math.round(imageWidth * box.getWidth()), Math.round(imageHeight * box.getHeight())); } // Shows polygon at supplied location private void ShowPolygon(int imageHeight, int imageWidth, List<Point> points, Graphics2D g2d) { g2d.setColor(new Color(0, 0, 0)); Polygon polygon = new Polygon(); // Construct polygon and display for (Point point : points) { polygon.addPoint((Math.round(point.getX() * imageWidth)), Math.round(point.getY() * imageHeight)); } g2d.drawPolygon(polygon); } // Draws only the vertical lines in the supplied polygon. private void ShowPolygonVerticals(int imageHeight, int imageWidth, List<Point> points, Graphics2D g2d) { g2d.setColor(new Color(0, 212, 0)); Object[] parry = points.toArray(); g2d.setStroke(new BasicStroke(2)); g2d.drawLine(Math.round(((Point) parry[0]).getX() * imageWidth), Math.round(((Point) parry[0]).getY() * imageHeight), Math.round(((Point) parry[3]).getX() * imageWidth), Math.round(((Point) parry[3]).getY() * imageHeight)); g2d.setColor(new Color(255, 0, 0)); g2d.drawLine(Math.round(((Point) parry[1]).getX() * imageWidth), Math.round(((Point) parry[1]).getY() * imageHeight), Math.round(((Point) parry[2]).getX() * imageWidth), Math.round(((Point) parry[2]).getY() * imageHeight)); } //Displays information from a block returned by text detection and text analysis private void DisplayBlockInfo(Block block) { System.out.println("Block Id : " + block.getId()); if (block.getText()!=null) System.out.println(" Detected text: " + block.getText()); System.out.println(" Type: " + block.getBlockType()); if (block.getBlockType().equals("PAGE") !=true) { System.out.println(" Confidence: " + block.getConfidence().toString()); } if(block.getBlockType().equals("CELL")) { System.out.println(" Cell information:"); System.out.println(" Column: " + block.getColumnIndex()); System.out.println(" Row: " + block.getRowIndex()); System.out.println(" Column span: " + block.getColumnSpan()); System.out.println(" Row span: " + block.getRowSpan()); } System.out.println(" Relationships"); List<Relationship> relationships=block.getRelationships(); if(relationships!=null) { for (Relationship relationship : relationships) { System.out.println(" Type: " + relationship.getType()); System.out.println(" IDs: " + relationship.getIds().toString()); } } else { System.out.println(" No related Blocks"); } System.out.println(" Geometry"); System.out.println(" Bounding Box: " + block.getGeometry().getBoundingBox().toString()); System.out.println(" Polygon: " + block.getGeometry().getPolygon().toString()); List<String> entityTypes = block.getEntityTypes(); System.out.println(" Entity Types"); if(entityTypes!=null) { for (String entityType : entityTypes) { System.out.println(" Entity Type: " + entityType); } } else { System.out.println(" No entity type"); } if(block.getPage()!=null) System.out.println(" Page: " + block.getPage()); System.out.println(); } public static void main(String arg[]) throws Exception { // The S3 bucket and document String document = ""; String bucket = ""; // set provider credentials AWSCredentialsProvider credentialsProvider = new ProfileCredentialsProvider("default"); AmazonS3 s3client = AmazonS3ClientBuilder.standard().withCredentials(credentialsProvider) .withEndpointConfiguration( new EndpointConfiguration("","us-east-1")) .build(); // Get the document from S3 s3object = s3client.getObject(bucket, document); S3ObjectInputStream inputStream = s3object.getObjectContent(); BufferedImage image =; // Call DetectDocumentText EndpointConfiguration endpoint = new EndpointConfiguration( "", "us-east-1"); AmazonTextract client = AmazonTextractClientBuilder.standard().withCredentials(credentialsProvider) .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); } }
    Java V2

    The following example code displays the document and boxes around lines of detected text.

    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 in the line that creates the TextractClient with the name of your developer profile.

    import; import; import; import; import; import; import; import; import; import; import java.util.Iterator; import java.util.List; //snippet-end:[textract.java2._detect_s3_text.import] /** * Before running this Java V2 code example, set up your development environment, including your credentials. * * For more information, see the following documentation topic: * * */ public class DetectText { public static void main(String[] args) { final String usage = "\n" + "Usage:\n" + " <bucketName> <docName> \n\n" + "Where:\n" + " bucketName - The name of the Amazon S3 bucket that contains the document. \n\n" + " docName - The document name (must be an image, i.e., book.png). \n"; if (args.length != 2) { System.out.println(usage); System.exit(1); } String bucketName = args[0]; String docName = args[1]; Region region = Region.US_EAST_1; TextractClient textractClient = TextractClient.builder() .region(region) .credentialsProvider(ProfileCredentialsProvider.create("profile-name")) .build(); detectDocTextS3(textractClient, bucketName, docName); textractClient.close(); } // snippet-start:[textract.java2._detect_s3_text.main] public static void detectDocTextS3 (TextractClient textractClient, String bucketName, String docName) { try { S3Object s3Object = S3Object.builder() .bucket(bucketName) .name(docName) .build(); // Create a Document object and reference the s3Object instance Document myDoc = Document.builder() .s3Object(s3Object) .build(); DetectDocumentTextRequest detectDocumentTextRequest = DetectDocumentTextRequest.builder() .document(myDoc) .build(); DetectDocumentTextResponse textResponse = textractClient.detectDocumentText(detectDocumentTextRequest); for (Block block: textResponse.blocks()) { System.out.println("The block type is " +block.blockType().toString()); } DocumentMetadata documentMetadata = textResponse.documentMetadata(); System.out.println("The number of pages in the document is " +documentMetadata.pages()); } catch (TextractException e) { System.err.println(e.getMessage()); System.exit(1); } } // snippet-end:[textract.java2._detect_s3_text.main] }

    This AWS CLI command displays the JSON output for the detect-document-text 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 detect-document-text \ --document '{"S3Object":{"Bucket":"bucket","Name":"document"}}' \ --profile profile-name \ --region region

    The following example code displays the document and boxes around detected lines of text.

    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.

    #Detects text in a document stored in an S3 bucket. Display polygon box around text and angled text import boto3 import io from PIL import Image, ImageDraw def process_text_detection(s3_connection, client, bucket, document): #Get the document from S3 s3_object = s3_connection.Object(bucket,document) s3_response = s3_object.get() stream = io.BytesIO(s3_response['Body'].read()) #To process using image bytes: #image_binary = stream.getvalue() #response = client.detect_document_text(Document={'Bytes': image_binary}) # Detect text in the document # 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 print ('Detected Document Text') # Create image showing bounding box/polygon the detected lines/text for block in blocks: # Display information about a block returned by text detection 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') # Display the image return len(blocks) def main(): session = boto3.Session(profile_name='profile-name') s3_connection = session.resource('s3') client = session.client('textract', region_name='region') bucket = '' document = '' block_count=process_text_detection(s3_connection,client,bucket,document) print("Blocks detected: " + str(block_count)) if __name__ == "__main__": main()

    The following Node.js example code displays the document and boxes around detected lines of text. It outputs an image of the results to the directory you run the code from. It makes use of the image-size and images packages.

    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 the value of regionConfig with the name of the region your account is in. Replace the value of credentialswith the name of your developer profile.

    //Copyright 2018, Inc. or its affiliates. All Rights Reserved. //PDX-License-Identifier: MIT-0 (For details, see 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"); // Set variables var credentials = new AWS.SharedIniFileCredentials({profile: 'default'}); AWS.config.credentials = credentials; AWS.config.update({region:'region-name'}); const bucket = 'bucket-name' // the s3 bucket name const photo = 'photo-name' // the name of file // Create a canvas and get the context const { createCanvas } = require('canvas') const canvas = createCanvas(200, 200) const ctx = canvas.getContext('2d') // 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)"output-image.jpg"); } catch (err){ console.error(err);} } main()
  4. Run the example. The Python and Java examples display the document image. A black box surrounds each line of detected text. A green vertical line is the start of a detected word. A red vertical line is the end of a detected word. The AWS CLI example displays only the JSON output for the DetectDocumentText operation.