In this post, we present a solution for digitizing transactional documents using Amazon Textract and incorporate a human review using Amazon Augmented AI (A2I). You can find the solution source at our GitHub repository.
Organizations must frequently process scanned transactional documents with structured text so they can perform operations such as fraud detection or financial approvals. Some common examples of transactional documents that contain tabular data include bank statements, invoices, and bills of materials. Manually extracting data from such documents is expensive, time-consuming, and often requires a significant investment in training a specialized workforce. With the architecture outlined in this post, you can digitize tabular data from even low-quality scanned documents and achieve a high degree of accuracy.
Significant strides have been made with machine learning (ML)-based algorithms to increase accuracy and reliability when processing scanned text documents. These algorithms often match human-level performance in recognizing text and extracting content.

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