Amazon Textract Features

Textract goes beyond traditional OCR and provides a lot of ML capabilities that make information extraction efficient, smart, and fast. Here are a few highlights.

Automating Forms Input

In traditional OCR, without hard coding bounding boxes or implementing complex logic, it isn’t possible to retain relationships between the parts of text extracted from a given document.

Textract understands forms and extracts text blocks, maintaining relationships between them. On an image of the form, Textract will output a key-value pair with these attributes as key and appropriate values against each of them.

As seen above, employment application form that has employe’s full name, phone number, house & mailing addresses, output from Textract will be a json object with key “Full Name” and text against associated to it as value.

Converting Tables

When extracting text from images of documents, Textract automatically detects tables and keeps the composition of data represented in those tables intact. The output of such data is in tabular format, which allows the user to store them easily in their databases. This is helpful for documents that are largely composed of structured data—such as financial reports or medical records—that have column names in the top row of the table followed by rows of individual entries.

Keep Reading Format Intact — Multi-Column

Consider an example of extracting news from old-age newspapers, where for more readability, text was presented in a multi-column format. Textract provides a bounding box and geometry around text blocks that allow users to place the extracted text in the desired manner.

Quality Assurance — Confidence Controls

Textract outputs the text blocks extracted with a confidence level. This allows users to make informed decisions about how they want to use the results. Users can set the level where human intervention is required for correction or verification, depending upon the requirements.