Amazon Textract is a service that automatically extract text and data from scanned documents. With Textract, you can quickly automate document workflows and process millions of document pages in hours.
- Upload a document in S3.
- S3 triggers the execution of a Lambda function (already done in Lab 0).
- The function use the synchronous Textract API (
DetectDocumentText). - Textract reads the document in S3 and extract the text from it.
- Textract return the extracted text to the Lambda function.
In Lambda console, click on your documentTextract-xyz function, scroll down to edit code inside the browser. Replace the code with the following one and click Deploy:
import urllib
import boto3
textract = boto3.client('textract')
def handler(event, context):
source_bucket = event['Records'][0]['s3']['bucket']['name']
object_key = urllib.parse.unquote_plus(event['Records'][0]['s3']['object']['key'])
textract_result = textract.detect_document_text(
Document={
'S3Object': {
'Bucket': source_bucket,
'Name': object_key
}
}
)
print(textract_result)We use the DetectDocumentText API to extract the text from an image (JPEG or PNG) retrieved in S3.
In order to test the process, you need to upload an image in the workshop-textract-xyz S3 bucket. In S3 console, click on your workshop-textract-xyz bucket, and click on Upload.
You can use one of those pictures:
- apollo11-summary.png
- apollo11.png which is slightly rotated
- apollo13.png with a 90 degree rotation
- apollo13-summary.png which contains underlining and overlining of some text.
If you go to CloudWatch logs, you will be able to display the output of your Lambda execution. You should get a json in the following form (details here):
{
"Blocks": [
{
"BlockType": "PAGE | LINE | WORD",
"Confidence": 99.9523696899414,
"Geometry": {
"BoundingBox": {
"Height": 0.02243288792669773,
"Left": 0.42624378204345703,
"Top": 0.04024827480316162,
"Width": 0.41624754667282104
}, ...
},
"Id": "a0730187-e3e5-46c5-973a-fb0c09c6ba00",
"Page": 1,
"Text": "part of the text detected",
...
}, ...
],
"DocumentMetadata": {
"Pages": 1
}
}It contains a list of Blocks, each Block representing an item recognized in the document (can either be a page, a line or a simple word). For each block you will retrieve its hierarchy in the document, its geometry (position and size), ... and obviously a text and the confidence score that Textract has in the accuracy of the recognized text.
Once we have the full details, we need to parse the result to extract the important data. In this case, we simply want the full text. In the documentTextract-xyz Lambda function, replace print(textract_result) with:
page=""
blocks = [x for x in textract_result['Blocks'] if x['BlockType'] == "LINE"]
for block in blocks:
page += " " + block['Text']
print(page)It's a simple way to retrieve all lines (BlockType = LINE) and to concatenate them. You can now test your function again to see the content of your document, loading a new test image or re-uploading the same as before. In CloudWatch logs, you will see the plain text, extracted from the picture you've just uploaded.
! TIPS ! Instead of parsing the result of textract on your own, you can use textract-extractor or textract-response-parser
! Note ! In this workshop, we only extracted plain text from the documents, but Amazon Textract is able to do more: You can extract key-value pairs from forms (such as regristration forms). Textract also detects and extract structured data, for example in tables, and keep this tabular format so that you can store it directly in a database. See https://aws.amazon.com/textract/features/ for details.
In this lab, we used Amazon Textract to extract text from a scanned document. In Lab 2, we will use Amazon Comprehend to extract information from the extracted text.