Introduction
The AI concept took an entirely new role in the modern world. While some managers fear these two words, other executives see in AI the must-have-tool. Indeed, even Forrester predicts the industry will continue to grow from $250 million in 2016 to $12 billion in 2023. Beyond these optimistic statistics, companies realised that AI can benefit the productivity of their activity. With the help of machine learning or deep learning, the tools such as robotic process automation, voice recognition, or OCR have seen the day. But, how do we go from AI to OCR? And what role does machine learning play in this whole process?
AI and ML, what is the connection?
Artificial intelligence
If we explain it as a simple way, we can say that artificial intelligence (AI) is the science that studies ways to build smart algorithms or machines. These are created in order to respond to a problematic situation, which usually requires a human brain to be solved.
However, AI remains much bigger than this short definition. Indeed, AI has developed itself over time, and continues to evolve. A simple chess-playing program, for example, was considered as a form of AI years ago. And, for a good reason, AI has gone far beyond chess-game strategies and overcame many skills that only human beings can develop. And nowadays, AI takes the place of the chess queen in the world of invention.
That is why tools such as Siri, Alexa, or Google Home came to light. All these new technologies have become strong actors of our daily life and machine learning rushingly appeared on this dynamic landscape.
Machine learning
Often confused with AI, machine learning (ML) is simply a subset of artificial intelligence. This tool gives the systems the ability to learn and improve themselves automatically from experience without being additionally programmed. One of the most relevant examples of machine learning experimentation is Deep Blue. The AI that defeated the world’s chess champion in 1977, used a method called “tree search algorithms” to evaluate millions of moves at every turn.
Thus, ML requires to work with datasets by examining and comparing the data to find common patterns exploring nuances — this is where the data scientist comes in. Another example of using machine learning is in the video prediction systems in platforms like Netflix, YouTube, and Amazon. As a more professional way, machine learning can also improve productivity of a company by being a part of OCR.
The role of Machine learning in OCR.
Machine learning and OCR
Before understanding the role of machine learning in OCR, we strongly recommend you to have a quick look at our previous article on OCR. It explains the basics of OCR and the major companies of this sector.
To understand how machine learning helps OCR, we need to clarify how OCR works. An optical character recognition is a tool that allows to recognize and select data from any document. You can use OCR in accounting or it can be also used in the bank industry for automating the loaning process and much more.
So, what is the role of machine learning in OCR? Let’s explore it! In order to recognize and classify the different data, the OCR system must be trained. For this, the system will follow 3 steps: training, testing, validating. The invoice example can help to understand this operation.
Example of Machine learning in OCR
In our example, we will analyze the data extracting from an invoice. Just imagine, we want to extract the tax. First, we are going to take a 100 invoices for the data training part. Then, the data scientist will use neural networks using deep learning showing to the systems where to find the “tax”. This operation will be repeated for 80 invoices, the 20 others are going to be used for the next part.
After teaching the systems where to found the tax, it’s now the turn to the algorithm to work. On the 20 last invoices, the algorithm mission will be to determine where is the data that it learned and just like that, we are already at the data test part.
Finally, the last step: data validation. As the name says, it’s the part where the human will confirm the work of the machine. Indeed, at the end of the data test move, the algorithm will announce an accuracy percentage so we could decide whether it appropriate for us. Let’s say the accuracy is 70%. The data scientist can either choose to accept such accuracy or to continue training and repeat processes till receive a more precise OCR.
If after reading this article, you would like to know how to apply OCR to your organization, at Young App, we guide companies in their digital transformation adenture with our OCR. We help to improve productivity by digitazing processes. You can first have a look to our website or even make an appointment. We will be more than happy to be your advisors.
