It was in 2011 when we first got acquainted with process automation. At that time, the acronym currently known as RPA (Robotic Process Automation) did not exist yet.
What did we do then? According to many, it was something innovative and in the words of others we simply did macros. The technology was not mature enough in order to extend its use, therefore, some praised us, and others labelled us to be all smoke and mirrors.
In retrospect the truth was that technology lacked development, consequently, we were confronted with scepticism due to the profound technical knowledge that was necessary to create a so-called ‘robot’. As a faithful follower of Peter Diamandis, we could say we were immersed in the second stage out of the six ‘Ds’ of exponential growth, as he describes in his magnificent book Bold. For those who are not familiar with Peter´s beliefs, he deemed that exponential growth has six stages which are: Digitalized, Deceptive, Disruptive, Demonetized, Dematerialized, Democratized. It is undeniable to acknowledge that since a technology is digitalized until the moment that it becomes democratized, its development is exponential – those who are reading this post from a digital device surely agree! 🙂
2011 was not exempt from challenges. Occasionally, features were simply not feasible to develop on the technical side (or even bureaucratic); process re-engineering used to ‘solve’ those limitations by including human intervention checkpoints when required. For example, an employee’s common task could be to read an image displayed by the robot and fill out a simple form with the requested information.
By using the previously described methodology and applying the technological improvements that came as time goes by, the first few ‘choices’ based on the use of OCR commenced to rise in 2015 – nonetheless, this technology thrived in barely any instance. It was in 2016 when we felt a change, the term RPA first appeared, referencing something that we were carrying out since 2011. We can not say that we are the pioneers as BluePrism, NICE, Automation Anywhere, or UiPath started the journey before us.
However, we are still tackling some of the limitations we faced seven years ago. For instance, experts have not been able to entrust the right data extraction from an image by using the latest version of Tesseract OCR Engine (currently version 4), whose extraction models are based on a Recurrent Neuronal Network (RNN) known as LSTM (Long Short-Term Memory). LSTM networks are frequently used in different applications to predict the evolution of temporal series, with the purpose of recognizing human speech, character sequences, among many other things.
Therefore, bearing all the previous outlined facts in mind, we have developed AIaaS (Artificial Intelligence as a Service), a cognitive library that aims to extract unstructured data from images. This technology can be used everywhere through an HTTP request to an API REST, in that way, the technical complexity that this technology usually conveys is non-existent.
AIaaS offers every user a combination of state-of-the-art models developed with the latest techniques on Machine Learning and Deep Learning, providing a solution to common issues as: detecting and extracting text inside of images – like reading a passport or a driving license – automatic identification of languages, classification and detection of objects within images, sentiment analysis, document classification, recognition of explicit content (violence, pornography), etc.
We are pleased to provide you with more details, it will certainly take longer than a single post. Are you interested? Please subscribe and you will receive the journals concerning all the development process and the challenges we have found.