2016 in a nutshellThe year kicked off with a spin-off of our Photomath app into an independent company. Something that started two years ago as a showcase of our state-of-the-art OCR technology grew into one of the most popular apps worldwide in the educational category!
As the year went by, Photopay was implemented in several new mobile banking apps in the CEE region. In the same time, we added support for documents from more than 100 countries to the BlinkID. Expansion of our customers’ network required gathering new talents so our team grew to 50.
International ExposureWe gained a trust of over 100 new companies from all over the world, from large enterprises to startups, and increased our revenue by 100%! By the end of the year, our technology was being used by millions of end users in over 60 countries.
The conferences and meetings with the industry leaders helped us better understand the financial industry challenges in 2017, especially when it comes to PSD2 in Europe. This had a big effect on our product development in order to put the client’s needs in the core of our innovation.
In Q3 & Q4 we proudly introduced two new products in our OCR family and once again proved that machine vision surpasses the obstacle of manual data entry and enables the best user experience for mobile apps.
The first one is an enhancement of a well-known BlinkID product. To our simple and fast ID scanner we added a new authentication functionality. Identity check is enabled in 3 simple steps in order to easily onboard new customers - scanning and extracting data from the ID, liveness test and face matching.
The other one is BlinkReceipt, real-time receipt scanner that enables capturing a complete retail receipt and extracting data on an item level. Very useful for personal finances management, marketing analysis or loyalty programs.
So, what’s cooking in 2017?We will continue to market new, cutting-edge technology products. In 2017 we’re investing a great deal of time and resources in developing learning systems in order to upgrade our OCR to the new machine learning technology. Machine learning represents a turning point in the way problems are solved in computer science, and it has caught a lot of attention within the academic community in last couple of years.
With the new approach our existing real-time text recognition will be even more accurate but, more importantly, we’ll be able to add new features and cover new use-cases, languages and various documents more easily.