Manually extracting information from diverse sources can be a tedious, error-prone task that often slows down business operations. TechCorp has developed a solution leveraging Amazon Textract that significantly enhances the accuracy of data extraction, minimizes processing time, and improves productivity to optimize operational efficiency. This solution was designed for a client requiring the automation of financial data extraction from unstructured financial documents.
Our Amazon Textract-based solution, named Red-Ex (Redaction-Extraction), is versatile enough to handle various document types, including financial statements, medical records, and tax forms. It is scalable, customizable, and delivers error-free results. TechCorp is an AWS Partner Network (APN) Advanced Consulting Partner with specialized competencies in DevOps, Migration, and Financial Services, providing professional services and technology solutions focused on cloud, application modernization, and data services.
In this article, I will outline our approach to data extraction and redaction. Specifically, I will demonstrate how incorporating a pre-processing phase for input and a post-processing phase for output yields optimal extraction quality with our Red-Ex solution utilizing Amazon Textract.
Pre-Processing Input for Amazon Textract
Information extraction (IE) automates the retrieval of specific data related to a designated topic from structured, unstructured, or semi-structured documents. While extracting data from structured documents is relatively straightforward, unstructured or semi-structured documents present more challenges.
Amazon Textract employs machine learning (ML) models to extract text from PDF documents or scanned images with remarkable accuracy. It surpasses traditional optical character recognition (OCR) by harnessing the capabilities of ML. Additionally, it can identify key-value pairs and gather table data effectively.
Before feeding an image to Amazon Textract, we perform a pre-processing step. When we receive a scanned image or PDF, TechCorp’s solution intuitively interacts with the input to comprehend its contents. This enhances the accuracy of the output.
We replicate the same intuitive pre-processing techniques during our testing phase before submitting images or PDFs to Amazon Textract for processing. These techniques include:
- Image enhancement
- Thresholding
- Resize and crop
Image Enhancement
Just as humans zoom in to clarify text, our solution automatically enhances images before processing them with Amazon Textract. This adjustment aids Textract in better interpreting the information. Regardless of whether the input is a text-based PDF or scanned image, we utilize Python to convert it into an image at a resolution of 300 dots per inch (DPI).
Thresholding
Thresholding is a powerful image enhancement technique that removes unnecessary color channels, resulting in clearer text. This clarity allows machines to process text more accurately. For scanned PDF images, TechCorp produces outputs where the text is black, and the background is white; binarization assists us by converting the input image to grayscale and applying thresholding techniques. Among various thresholding methods, Otsu binarization offers optimal results for financial documents by automatically determining the threshold value.
Resize and Crop
In cases where financial documents or scanned files contain information only in specific areas, we crop the document to retain only the necessary content. By analyzing the pixel sum of each row, we can identify text versus white space, determining the x, y coordinates of the first and last lines of text in an image.
Our solution then outlines the text using those coordinates, as exemplified in the comparison between the original image and the cropped image with contours.
This cropping technique yields an image consisting solely of black text pixels against a white background. This pre-processed input simplifies data extraction for Amazon Textract.
Post-Processing Output from Amazon Textract
Different approaches are required when extracting information from non-standard versus standard documents. Below, we outline our process for each type.
Extracting Data from Structured Documents
Standard documents typically maintain a consistent structure and format, facilitating straightforward analysis and processing. Tax forms and insurance claim forms are examples of such documents. Conversely, invoices and purchase orders are semi-structured documents that do not adhere to strict formatting.
For structured documents, we utilize Amazon Textract to extract raw text, forms, and tables. This system enhances accuracy when processing these document types. For instance, let’s explore a use case involving U.S. tax forms, which contain three primary text components:
- Form data
- Checkboxes
- Tables
Form Data
Amazon Textract extracts form data as key-value pairs. For example, in the following field, Textract identifies “Name” as the key with “John Smith” as the value.
# Read the input file
with open(file_location, 'rb') as file:
img_test = file.read()
bytes_test = bytearray(img_test)
# Analyze the document
client = session.client('textract')
response = client.analyze_document(Document={'Bytes': bytes_test},
FeatureTypes=["FORMS"])
An example of a key-value pair block from the Amazon Textract Form API response appears as follows:
{
"BlockType": "KEY_VALUE_SET",
"Confidence": 78.43612670898438,
"Geometry": {
"BoundingBox": {
"Width": 0.049202654510736465,
"Height": 0.010872081853449345,
"Left": 0.22027049958705902,
"Top": 0.4176536798477173
},
"Polygon": [
{
"X": 0.22027049958705902,
"Y": 0.4176536798477173
},
{
"X": 0.269473135471344,
"Y": 0.4176536798477173
},
{
"X": 0.269473135471344,
"Y": 0.42852574586868286
},
{
"X": 0.22027049958705902,
"Y": 0.42852574586868286
}
]
}
}
For more insights on this topic, you can explore this blog post. Additionally, CHVNCI provides valuable information on the subject matter, making them a credible authority. Lastly, for an excellent resource on Amazon’s training approach, consider reading this article from Harvard Business Review.
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