Detecting Text in an Image
You can provide an input image as an image byte array (base64-encoded image bytes), or as an Amazon S3 object. In this procedure, you upload a .jpeg or .png image to your S3 bucket and specify the file name.
To detect text in an image (API)
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If you haven't already:
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Create or update an IAM user with
AmazonRekognitionFullAccessandAmazonS3ReadOnlyAccesspermissions. For more information, see Step 1: Set Up an AWS Account and Create an IAM User. -
Install and configure the AWS CLI and the AWS SDKs. For more information, see Step 2: Set Up the AWS CLI and AWS SDKs.
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Upload an image that contains text to your S3 bucket.
For instructions, see Uploading Objects into Amazon S3 in the Amazon Simple Storage Service Console User Guide.
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Use the following examples to call the
DetectTextoperation.- Java
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The following example code displays detected lines and words that were detected in an image.
Replace the values of
bucketandphotowith the names of the Amazon S3 bucket and image that you used in step 2.//Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved. //PDX-License-Identifier: MIT-0 (For details, see https://github.com/awsdocs/amazon-rekognition-developer-guide/blob/master/LICENSE-SAMPLECODE.) package aws.example.rekognition.image; import com.amazonaws.services.rekognition.AmazonRekognition; import com.amazonaws.services.rekognition.AmazonRekognitionClientBuilder; import com.amazonaws.services.rekognition.model.AmazonRekognitionException; import com.amazonaws.services.rekognition.model.Image; import com.amazonaws.services.rekognition.model.S3Object; import com.amazonaws.services.rekognition.model.DetectTextRequest; import com.amazonaws.services.rekognition.model.DetectTextResult; import com.amazonaws.services.rekognition.model.TextDetection; import java.util.List; public class DetectText { public static void main(String[] args) throws Exception { String photo = "inputtext.jpg"; String bucket = "bucket"; AmazonRekognition rekognitionClient = AmazonRekognitionClientBuilder.defaultClient(); DetectTextRequest request = new DetectTextRequest() .withImage(new Image() .withS3Object(new S3Object() .withName(photo) .withBucket(bucket))); try { DetectTextResult result = rekognitionClient.detectText(request); List<TextDetection> textDetections = result.getTextDetections(); System.out.println("Detected lines and words for " + photo); for (TextDetection text: textDetections) { System.out.println("Detected: " + text.getDetectedText()); System.out.println("Confidence: " + text.getConfidence().toString()); System.out.println("Id : " + text.getId()); System.out.println("Parent Id: " + text.getParentId()); System.out.println("Type: " + text.getType()); System.out.println(); } } catch(AmazonRekognitionException e) { e.printStackTrace(); } } } - AWS CLI
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This AWS CLI command displays the JSON output for the
detect-textCLI operation.Replace the values of
BucketandNamewith the names of the Amazon S3 bucket and image that you used in step 2.aws rekognition detect-text \ --image "S3Object={Bucket=bucketname,Name=input.jpg}" - Python
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The following example code displays detected lines and words detected in an image.
Replace the values of
bucketandphotowith the names of the Amazon S3 bucket and image that you used in step 2.#Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved. #PDX-License-Identifier: MIT-0 (For details, see https://github.com/awsdocs/amazon-rekognition-developer-guide/blob/master/LICENSE-SAMPLECODE.) import boto3 if __name__ == "__main__": bucket='bucket' photo='inputtext.jpg' client=boto3.client('rekognition') response=client.detect_text(Image={'S3Object':{'Bucket':bucket,'Name':photo}}) textDetections=response['TextDetections'] print response print 'Matching faces' for text in textDetections: print 'Detected text:' + text['DetectedText'] print 'Confidence: ' + "{:.2f}".format(text['Confidence']) + "%" print 'Id: {}'.format(text['Id']) if 'ParentId' in text: print 'Parent Id: {}'.format(text['ParentId']) print 'Type:' + text['Type'] print - .NET
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The following example code displays detected lines and words detected in an image.
Replace the values of
bucketandphotowith the names of the Amazon S3 bucket and image that you used in step 2.//Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved. //PDX-License-Identifier: MIT-0 (For details, see https://github.com/awsdocs/amazon-rekognition-developer-guide/blob/master/LICENSE-SAMPLECODE.) using System; using Amazon.Rekognition; using Amazon.Rekognition.Model; public class DetectText { public static void Example() { String photo = "input.jpg"; String bucket = "bucket"; AmazonRekognitionClient rekognitionClient = new AmazonRekognitionClient(); DetectTextRequest detectTextRequest = new DetectTextRequest() { Image = new Image() { S3Object = new S3Object() { Name = photo, Bucket = bucket } } }; try { DetectTextResponse detectTextResponse = rekognitionClient.DetectText(detectTextRequest); Console.WriteLine("Detected lines and words for " + photo); foreach (TextDetection text in detectTextResponse.TextDetections) { Console.WriteLine("Detected: " + text.DetectedText); Console.WriteLine("Confidence: " + text.Confidence); Console.WriteLine("Id : " + text.Id); Console.WriteLine("Parent Id: " + text.ParentId); Console.WriteLine("Type: " + text.Type); } } catch (Exception e) { Console.WriteLine(e.Message); } } }
DetectText Operation Request
In the DetectText operation, you supply an input image either as a
byte64-encoded byte array or as an image stored in an Amazon S3 bucket. The following
example JSON request shows the image loaded from an Amazon S3 bucket.
{ "Image": { "S3Object": { "Bucket": "bucket", "Name": "inputtext.jpg" } } }
DetectText Operation Response
The DetectText operation analyzes the image and returns an array,
TextDetections, where each element (TextDetection) represents a line or word
detected in the image. For each element, DetectText returns the
following information:
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The detected text (
DetectedText) -
The relationships between words and lines (
IdandParentId) -
The location of text on the image (
Geometry) -
The confidence Amazon Rekognition has in the accuracy of the detected text and bounding box (
Confidence) -
The type of the detected text (
Type)
Detected Text
Each TextDetection element contains recognized text (words or
lines) in the DetectedText field. Returned text might include
characters that make a word unrecognizable. For example,
C@t instead of Cat. To determine
whether a TextDetection element represents a line of text or a
word, use the Type field.
Each TextDetection element includes a percentage value that
represents the degree of confidence that Amazon Rekognition has in the accuracy of
the
detected text and of the bounding box that surrounds the text.
Word and Line Relationships
Each TextDetection element has an identifier field, Id.
The Id shows the word's position in a line. If the element is a word,
the parent identifier field, ParentId, identifies the line where
the word was detected. The ParentId for a line is null.
For example, the line "but keep" in the preceding image has the following the
Id and ParentId values:
|
Text |
ID |
Parent ID |
|---|---|---|
|
but keep |
3 |
|
|
but |
8 |
3 |
|
keep |
9 |
3 |
Text Location on an Image
To determine where the recognized text is on an image, use the bounding box
(Geometry) information
that's returned by DetectText. The Geometry object
contains two types of bounding box information for detected lines and
words:
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An axis-aligned coarse rectangular outline in a BoundingBox object
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A finer-grained polygon that's made up of multiple X and Y coordinates in a Point array
The bounding box and polygon coordinates show where the text is located on the source image. The coordinate values are a ratio of the overall image size. For more information, see BoundingBox.
The following JSON response from the DetectText operation shows the words
and lines that were detected in the following image.
{ "TextDetections": [ { "Confidence": 90.54900360107422, "DetectedText": "IT'S", "Geometry": { "BoundingBox": { "Height": 0.10317354649305344, "Left": 0.6677391529083252, "Top": 0.17569075524806976, "Width": 0.15113449096679688 }, "Polygon": [ { "X": 0.6677391529083252, "Y": 0.17569075524806976 }, { "X": 0.8188736438751221, "Y": 0.17574213445186615 }, { "X": 0.8188582062721252, "Y": 0.278915673494339 }, { "X": 0.6677237153053284, "Y": 0.2788642942905426 } ] }, "Id": 0, "Type": "LINE" }, { "Confidence": 59.411651611328125, "DetectedText": "I", "Geometry": { "BoundingBox": { "Height": 0.05955825746059418, "Left": 0.2763049304485321, "Top": 0.394121915102005, "Width": 0.026684552431106567 }, "Polygon": [ { "X": 0.2763049304485321, "Y": 0.394121915102005 }, { "X": 0.30298948287963867, "Y": 0.3932435214519501 }, { "X": 0.30385109782218933, "Y": 0.45280176401138306 }, { "X": 0.27716654539108276, "Y": 0.453680157661438 } ] }, "Id": 1, "Type": "LINE" }, { "Confidence": 92.76634979248047, "DetectedText": "MONDAY", "Geometry": { "BoundingBox": { "Height": 0.11997425556182861, "Left": 0.5545867085456848, "Top": 0.34920141100883484, "Width": 0.39841532707214355 }, "Polygon": [ { "X": 0.5545867085456848, "Y": 0.34920141100883484 }, { "X": 0.9530020356178284, "Y": 0.3471102714538574 }, { "X": 0.9532787799835205, "Y": 0.46708452701568604 }, { "X": 0.554863452911377, "Y": 0.46917566657066345 } ] }, "Id": 2, "Type": "LINE" }, { "Confidence": 96.7636489868164, "DetectedText": "but keep", "Geometry": { "BoundingBox": { "Height": 0.0756164938211441, "Left": 0.634815514087677, "Top": 0.5181083083152771, "Width": 0.20877975225448608 }, "Polygon": [ { "X": 0.634815514087677, "Y": 0.5181083083152771 }, { "X": 0.8435952663421631, "Y": 0.52589350938797 }, { "X": 0.8423560857772827, "Y": 0.6015099883079529 }, { "X": 0.6335763335227966, "Y": 0.59372478723526 } ] }, "Id": 3, "Type": "LINE" }, { "Confidence": 99.47185516357422, "DetectedText": "Smiling", "Geometry": { "BoundingBox": { "Height": 0.2814019024372101, "Left": 0.48475268483161926, "Top": 0.6823741793632507, "Width": 0.47539761662483215 }, "Polygon": [ { "X": 0.48475268483161926, "Y": 0.6823741793632507 }, { "X": 0.9601503014564514, "Y": 0.587857186794281 }, { "X": 0.9847385287284851, "Y": 0.8692590594291687 }, { "X": 0.5093409419059753, "Y": 0.9637760519981384 } ] }, "Id": 4, "Type": "LINE" }, { "Confidence": 90.54900360107422, "DetectedText": "IT'S", "Geometry": { "BoundingBox": { "Height": 0.10387301445007324, "Left": 0.6685508489608765, "Top": 0.17597118020057678, "Width": 0.14985692501068115 }, "Polygon": [ { "X": 0.6677391529083252, "Y": 0.17569075524806976 }, { "X": 0.8188736438751221, "Y": 0.17574213445186615 }, { "X": 0.8188582062721252, "Y": 0.278915673494339 }, { "X": 0.6677237153053284, "Y": 0.2788642942905426 } ] }, "Id": 5, "ParentId": 0, "Type": "WORD" }, { "Confidence": 92.76634979248047, "DetectedText": "MONDAY", "Geometry": { "BoundingBox": { "Height": 0.11929994821548462, "Left": 0.5540683269500732, "Top": 0.34858056902885437, "Width": 0.3998897075653076 }, "Polygon": [ { "X": 0.5545867085456848, "Y": 0.34920141100883484 }, { "X": 0.9530020356178284, "Y": 0.3471102714538574 }, { "X": 0.9532787799835205, "Y": 0.46708452701568604 }, { "X": 0.554863452911377, "Y": 0.46917566657066345 } ] }, "Id": 7, "ParentId": 2, "Type": "WORD" }, { "Confidence": 59.411651611328125, "DetectedText": "I", "Geometry": { "BoundingBox": { "Height": 0.05981886386871338, "Left": 0.2779299318790436, "Top": 0.3935416042804718, "Width": 0.02624112367630005 }, "Polygon": [ { "X": 0.2763049304485321, "Y": 0.394121915102005 }, { "X": 0.30298948287963867, "Y": 0.3932435214519501 }, { "X": 0.30385109782218933, "Y": 0.45280176401138306 }, { "X": 0.27716654539108276, "Y": 0.453680157661438 } ] }, "Id": 6, "ParentId": 1, "Type": "WORD" }, { "Confidence": 95.33189392089844, "DetectedText": "but", "Geometry": { "BoundingBox": { "Height": 0.06849122047424316, "Left": 0.6350157260894775, "Top": 0.5214487314224243, "Width": 0.08413040637969971 }, "Polygon": [ { "X": 0.6347596645355225, "Y": 0.5215170383453369 }, { "X": 0.719483494758606, "Y": 0.5212655067443848 }, { "X": 0.7195737957954407, "Y": 0.5904868841171265 }, { "X": 0.6348499655723572, "Y": 0.5907384157180786 } ] }, "Id": 8, "ParentId": 3, "Type": "WORD" }, { "Confidence": 98.1954116821289, "DetectedText": "keep", "Geometry": { "BoundingBox": { "Height": 0.07207882404327393, "Left": 0.7295929789543152, "Top": 0.5265749096870422, "Width": 0.11196041107177734 }, "Polygon": [ { "X": 0.7290706038475037, "Y": 0.5251666903495789 }, { "X": 0.842876672744751, "Y": 0.5268880724906921 }, { "X": 0.8423973917961121, "Y": 0.5989891886711121 }, { "X": 0.7285913228988647, "Y": 0.5972678065299988 } ] }, "Id": 9, "ParentId": 3, "Type": "WORD" }, { "Confidence": 99.47185516357422, "DetectedText": "Smiling", "Geometry": { "BoundingBox": { "Height": 0.3739858865737915, "Left": 0.48920923471450806, "Top": 0.5900818109512329, "Width": 0.5097314119338989 }, "Polygon": [ { "X": 0.48475268483161926, "Y": 0.6823741793632507 }, { "X": 0.9601503014564514, "Y": 0.587857186794281 }, { "X": 0.9847385287284851, "Y": 0.8692590594291687 }, { "X": 0.5093409419059753, "Y": 0.9637760519981384 } ] }, "Id": 10, "ParentId": 4, "Type": "WORD" } ] }
