It’s no surprise that enterprises today are sitting on a goldmine of data. Artificial Intelligence has been garnering media hype recently, and it’s no longer in the experimental phase as more and more businesses are adopting it as essential to their business.
As per statistics, annual AI software revenue will touch the $100 billion mark by 2025. Hence, organizations are expanding their spending plans to leverage what AI has to offer optimally.
AI in document management sounds like a great possibility for extracting data from complex and visually rich documents. AI technologies such as Document Digitization, Computer Vision, Natural Language Processing, and Intelligent Search provide a host of benefits when applied to document management and help businesses succeed in today’s volatile market conditions.
Data digitization: There are tons of paper documents, unstructured data, and unprocessed data lying there that enterprises cannot use. Different elements and visual objects are present in the data, such as graphs, charts and tables, logos, and text. Hence the first step is the digitization of data.
Data extraction: AI document processing solutions dig deeper to bring out subtle variations and granular differences invisible to the naked eye. And the process of fetching data from a library of documents is done in nanoseconds.
The next step is to make sense of the data and get a better sense of the information. Then enterprises try to understand the context and also conduct document classification – for instance, if someone uploads a bunch of documents, which is a collection of checks, invoices, purchases, or sales orders, Document AI automatically separates them.
Data analytics: AI has immense potential when it comes to data analytics. AI document analysis collates and extracts data in massive quantities and derives meaningful insights using predictive analytics and data visualization. Such actionable insights improve decision-making and optimize processes significantly.
Technology has come a long way from CAPTCHA boxes asking for random words to be typed to prove you are a human. Today, it is digitizing books, decoding, and interpreting various media types, which are no longer restricted to plain text.
Broadly, documents are categorized into the following types:
So far, AI in document management has proved worthy of extracting information from text-based documents. But how can enterprises capture insights from visually-rich media?
For AI to extract the data helpfully, it not only has to understand contextual clues from the text itself but also be primed to handle and interpret elements such as images, logos, symbols, charts, and captions, among others.
Here, Optical Character Recognition might not be cut out because it requires consistency in document formats. Advanced AI technologies such as computer vision and NLP models are ready to compensate for OCR’s disadvantages and help enterprises decode millions of documents accurately.
Hence, document understanding is much more than OCR or handwriting recognition and requires the capability to detect & recognize various structural elements in enterprise documents. With computer vision and NLP models combined, AI can make document extraction, processing, and comprehension a breeze.