Ashley Keil, IBML’s VP of Sales, EMEA/APAC, discusses how artifi cial intelligence platforms can radically change, improve and automate how documents are handled and processed.

Don’t panic! You might recall the famous inscription on the cover of The Hitchhiker’s Guide to the Galaxy, a classic sci-fi book written by the late Douglas Adams, which charts the adventures of Arthur Dent and Ford Prefect after Vogon’s demolish Earth to make way for a new hyperspace bypass.

Published in 1979, what might not be quite so well-known, is just how prescient Adams was in terms of referencing technology which has subsequently been developed. The fi ctitious Hitchhiker’s Guide itself was almost a precursor to the Kindle – a handheld electronic book able to serve a million pages via a four inch square screen. The information stored in it is user-generated and constantly updated – exactly the approach adopted by Wikipedia - and the Babel Fish introduced the idea of putting something in your ear which could then translate languages – a concept actually brought to market in 2016 by Waverley Labs with the Pilot Smart Earbuds.

And talking of Babel Fish, rapid developments involving self-learning artifi cial intelligence platforms [AI] – which solve complex problems automatically - are now enabling information and business managers to quickly gain real insight from documents irrespective of the language, the computer fi le format used and whether documents contain machine print or cursive handwriting or both.

This is radically set to change how organisations cope with recognising and classifying millions of documents and then extracting and validating information without any manual intervention at all, thereby increasing productivity, accuracy and saving money.

Existing character recognition

technologies have their limitations To date, a vast amount has been invested deploying traditional recognition technologies such OCR, ICR and intelligent word recognition to analyse the content of documents and boost automation. It’s still very much a growth area. Research shows that the global OCR market is expected to reach $13.38bn by 2025 – increasing at a CAGR of 13.7% from 2019.

Despite this, there are limitations. Many ICR/OCR engines struggle to process a mix of documents – encompassing structured, semi-structured and unstructured data – along with cursive handwriting, historical and old documents especially when the legibility of the paperwork is poor. The situation is exacerbated when volumes are high. And no one traditional ICR/OCR engine can

32 | TOMORROW’S FM “Research shows that the global

OCR market is expected to reach $13.38bn by 2025 – increasing at a CAGR of 13.7% from 2019.”

This is achieved given a feedback ‘re-training loop’ is used - think of it as supervised learning overseen by a human - whereby errors in the system are corrected when they arise so that the inference (and the meta data underlying it) updates, learns and is able to then deal with similar situations on its own when they next appear.

seamlessly process a variety of languages – jumping from documents in English to Chinese, German and so on.

With such variability, correct read-rates drop markedly – it’s still tough to get more than 90-95% accuracy today - such that staff are required to then manually rekey information in. This is time consuming, costly and begs the question of whether enough trained employees are available to do it.

Of course, crowd-sourcing approaches are a good and cheaper work around than actually hiring people to enhance accuracy. Snippets of data are sent to online entry clerks logged into an internet-based system who then check it prior to inputting it into line of business systems.

But the promise - and now reality - of AI is that these challenges are also resolved using powerful cognitive systems.

AI-powered solutions are available

today for document processing Utilising neural networks, AI-driven document processing platforms offer a leapfrog advance over traditional recognition technologies. At the outset, a system is ‘trained’ so that a consolidated core knowledge base is created about a particular (spoken) language, form and/or document type. In AI jargon, this is known as the ‘inference’. This knowledge base then expands and grows over time as more and more information is fed into the system and it self-learns – able to recognise documents and their contents as they arrive.

Page 1  |  Page 2  |  Page 3  |  Page 4  |  Page 5  |  Page 6  |  Page 7  |  Page 8  |  Page 9  |  Page 10  |  Page 11  |  Page 12  |  Page 13  |  Page 14  |  Page 15  |  Page 16  |  Page 17  |  Page 18  |  Page 19  |  Page 20  |  Page 21  |  Page 22  |  Page 23  |  Page 24  |  Page 25  |  Page 26  |  Page 27  |  Page 28  |  Page 29  |  Page 30  |  Page 31  |  Page 32  |  Page 33  |  Page 34  |  Page 35  |  Page 36  |  Page 37  |  Page 38  |  Page 39  |  Page 40  |  Page 41  |  Page 42  |  Page 43  |  Page 44  |  Page 45  |  Page 46  |  Page 47  |  Page 48  |  Page 49  |  Page 50  |  Page 51  |  Page 52  |  Page 53  |  Page 54  |  Page 55  |  Page 56  |  Page 57  |  Page 58  |  Page 59  |  Page 60  |  Page 61  |  Page 62  |  Page 63  |  Page 64  |  Page 65  |  Page 66  |  Page 67  |  Page 68  |  Page 69  |  Page 70  |  Page 71  |  Page 72  |  Page 73  |  Page 74