Modernizing Document Data Extraction with AI

Extracting data from documents has evolved significantly since the OCR days of the 1990s. Template-based approaches have been replaced with AI- (artificial intelligence) and NLP- (natural language processing) guided systems, offering intelligent data extraction from complex unstructured documents. Intelligent Data Extraction (IDE) typically is a component of an overall Intelligent Automation (IA) strategy that combines various processes to give the organization a complete, automated end-to-end solution. Following the steps outlined below can simplify the journey to a successful IDE solution as part of a more comprehensive, overall Intelligent Automation solution.

Most IDE solutions support various inputs — such as multiple languages, handwriting, signature validation, barcodes, free-form and tabular data, and numerous image formats. Using low-code GUI-based applications, the non-data scientist can build extraction processes for basic inputs.

For more complex documents, your planning process will include an in-depth review of the documents and their attributes, determining the fields to extract, and deciding on the downstream processes post-extraction. Other common planning tasks include; processes for exception handling, Validation, and exporting the data, including inserting the extracted data into downstream systems.

A successful IDE implementation requires an understanding of these tasks and planning for their execution.

The extraction process is broken down into three main processes: Read, Refine, Apply. Each process includes a series of tasks. The tasks themselves may lend themselves to more than one process, making the delineation between the main processes a bit fuzzy, but the tasks remain. These tasks are commonly automated within the IDE system but can be configured to enhance the data capture process:

1. Document Import — Documents flow to the IDE platform from multiple source systems through various methods, including different automation options, workflow queues, managed folders, or direct feeds,

2. Image Enhancement — Not all documents will be clean PDFs or images. Document images, including faxes and pictures, may be captured at lower resolutions making the extraction process less accurate. Your IDE solution should automatically evaluate the image resolution and enhance it as needed. You may need to plan for an exception process for low-quality or heavily marked-up images.

3. Document Classification — Your IDE platform works across multiple use cases with a wide variety of documents. Incoming documents are classified based upon page attributes. Plan a clear document hierarchy to delineate your use cases. Defining document attributes allows for better classification. Identify unique characteristics for each document type to assist in the classification process,

4. Character / Word Recognition — Your IDE platform supports multiple languages, handwriting, barcodes, word recognition in both key-value pairs and unstructured text.