A business cannot run in the absence of comprehensive data. Unfortunately, unstructured data in non-digital formats serve as a significant roadblock in apt decision-making, which RPA tools cannot help enterprises save time.
That’s why enterprises today are turning to data digitization, which implies retrieving information from bulk, unstructured data faster and quicker. And the process has a name; it is called Intelligent Information Extraction.
The whole concept of businesses dealing with bulk paperwork and entering data manually into their IT systems has become old school. Today, data extraction using various Intelligent Document Processing software has become mandatory to keep up with the changing market trends.
Technologies such as optical character recognition (OCR) and handwriting recognition help extract data easily but have their share of challenges. However, with the advancements in deep learning and cloud systems, a new breed of text digitization solutions has emerged. These solutions enable enterprises to easily capture the connection between field labels, values, the structure, and the layout of data elements – from boxes and tables to specific details like checkboxes and signatures.
This entire process falls under Intelligent Information Extraction.
OCR or Optical Character Recognition detects text regions on any scanned images and converts those regions into the correct digital text. With the help of cognitive AI, OCR can extract data from pages featuring handwritten text.
Contrarily, extracting information from unstructured text using NLP algorithms and automation fetches essential information into more editable and structured data formats. Intelligent Information Extraction facilitates the whole process.
OCR data extraction has a couple of limitations, a few of which are illustrated below:
Intelligent Information Extraction is responsible for overriding the challenges mentioned above and helping businesses with clear and concise structured data.
There are various use cases for data digitization, a few of which are discussed below:
Form Digitization: Multi-page paper-based forms involving a mix of typed text, handwriting, checkboxes, and other fields and tables can be better optimized for data extraction using intelligent data extraction software.
Touch-free Zero Template Extraction: Data digitization of non-standard and unstructured input documents containing information in varying layouts addresses manual or OCR data extraction challenges.
Mixed-type Documents: Documents in different formats and types make data extraction a herculean task; not with an Intelligent Information Extraction approach.
Information Consistency Checking: The most complex use case requiring mature products and covering previous use cases, including consistency verification rules, can fare well with data digitization.
A comprehensive data digitization solution to extract information intelligently should comprise certain basic features. Some of them are listed below:
Businesses need intelligent data that’s well-structured and formatted to cater to data-driven decisions. Therefore, data digitization using intelligent information extraction features, like the ones mentioned above, can prove game-changing for enterprises.
Let’s explore a few benefits of using data extraction software for capturing granular data from structured and unstructured data sources.
To deliver stellar customer experiences, businesses need data to make improved business decisions. However, manual input and extraction of unstructured data is time and cost-intensive, and the outcome is often clouded by errors. Intelligent information extraction can address such challenges and help businesses make informed decisions faster than usual. Data digitization is the need of the hour; sooner businesses realize, it is better for them to gain an edge in the competition.