Table extraction and processing

Amazon Textract can detect tables and their content. A company can extract all the amounts from an expense report and apply rules, such as any expense more than $1000 needs extra review.

The following code example uses the expense report sample document and prints the content of each cell, along with a warning message if any expense is more than $1000.

import boto3
from trp import Document

# Document
s3BucketName = "ki-textract-demo-docs"
documentName = "expense.png"

# Amazon Textract client
textract = boto3.client('textract')

# Call Amazon Textract
response = textract.analyze_document(
    Document={
        'S3Object': {
            'Bucket': s3BucketName,
            'Name': documentName
        }
    },
    FeatureTypes=["TABLES"])

#print(response)

doc = Document(response)

def isFloat(input):
  try:
    float(input)
  except ValueError:
    return False
  return True

warning = ""
for page in doc.pages:
     # Print tables
    for table in page.tables:
        for r, row in enumerate(table.rows):
            itemName  = ""
            for c, cell in enumerate(row.cells):
                print("Table[{}][{}] = {}".format(r, c, cell.text))
                if(c == 0):
                    itemName = cell.text
                elif(c == 4 and isFloat(cell.text)):
                    value = float(cell.text)
                    if(value > 1000):
                        warning += "{} is greater than $1000.".format(itemName)
if(warning):
    print("\nReview needed:\n====================\n" + warning)

The following text is the output of the table cells and the text within.

Table[0][0] = Expense Description 
Table[0][1] = Type 
Table[0][2] = Date 
Table[0][3] = Merchant Name 
Table[0][4] = Amount (USD) 
Table[1][0] = Furniture (Desks and Chairs) 
Table[1][1] = Office Supplies 
Table[1][2] = 5/10/1019 
Table[1][3] = Merchant One 
Table[1][4] = 1500.00 
Table[2][0] = Team Lunch 
Table[2][1] = Food 
Table[2][2] = 5/11/2019 
Table[2][3] = Merchant Two 
Table[2][4] = 100.00 
Table[3][0] = Team Dinner 
Table[3][1] = Food 
Table[3][2] = 5/12/2019 
Table[3][3] = Merchant Three 
Table[3][4] = 300.00 
Table[4][0] = Laptop 
Table[4][1] = Office Supplies 
Table[4][2] = 5/13/2019 
Table[4][3] = Merchant Three 
Table[4][4] = 200.00 
Table[5][0] = 
Table[5][1] = 
Table[5][2] = 
Table[5][3] = 
Table[5][4] = 
Table[6][0] = 
Table[6][1] = 
Table[6][2] = 
Table[6][3] = 
Table[6][4] = 
Table[7][0] = 
Table[7][1] = 
Table[7][2] = 
Table[7][3] = 
Table[7][4] = 
Table[8][0] = 
Table[8][1] = 
Table[8][2] = 
Table[8][3] = Total 
Table[8][4] = 2100.00 

Review needed:
====================
Furniture (Desks and Chairs) is greater than $1000.

PDF document processing:

For the earlier examples, you used images with the sync API operations. Now, see how you can process PDF files using the async API operations.

First, use Start Document detection or Start Doument analysis to start an Amazon Textract job. As the job completes, AWS Textract publishes the results of an AWS Textract request, including completion status, to Amazon SNS. You can then use Get Document Text detection or Get Document Analysis to get the results from Amazon Textract.

The following code example shows how to start a job, get job status, and then process the results.

import boto3
import time

def startJob(s3BucketName, objectName):
    response = None
    client = boto3.client('textract')
    response = client.start_document_text_detection(
    DocumentLocation={
        'S3Object': {
            'Bucket': s3BucketName,
            'Name': objectName
        }
    })

    return response["JobId"]

def isJobComplete(jobId):
    # For production use cases, use SNS based notification 
    # Details at: https://docs.aws.amazon.com/textract/latest/dg/api-async.html
    time.sleep(5)
    client = boto3.client('textract')
    response = client.get_document_text_detection(JobId=jobId)
    status = response["JobStatus"]
    print("Job status: {}".format(status))

    while(status == "IN_PROGRESS"):
        time.sleep(5)
        response = client.get_document_text_detection(JobId=jobId)
        status = response["JobStatus"]
        print("Job status: {}".format(status))

    return status

def getJobResults(jobId):

    pages = []

    client = boto3.client('textract')
    response = client.get_document_text_detection(JobId=jobId)
    
    pages.append(response)
    print("Resultset page recieved: {}".format(len(pages)))
    nextToken = None
    if('NextToken' in response):
        nextToken = response['NextToken']

    while(nextToken):

        response = client.get_document_text_detection(JobId=jobId, NextToken=nextToken)

        pages.append(response)
        print("Resultset page recieved: {}".format(len(pages)))
        nextToken = None
        if('NextToken' in response):
            nextToken = response['NextToken']

    return pages

# Document
s3BucketName = "ki-textract-demo-docs"
documentName = "Amazon-Textract-Pdf.pdf"

jobId = startJob(s3BucketName, documentName)
print("Started job with id: {}".format(jobId))
if(isJobComplete(jobId)):
    response = getJobResults(jobId)

#print(response)

# Print detected text
for resultPage in response:
    for item in resultPage["Blocks"]:
        if item["BlockType"] == "LINE":
            print ('\033[94m' +  item["Text"] + '\033[0m')

The following image shows the job status as the API call proceeds.