Important steps have been made in recent years in the field of document processing automation and Business Process Outsourcing (BPO). This being in the field of automatic learning and natural language technology. And all in order to automate every possible task at companies and to focus efforts on other issues. Something that is feasible with our TAAD and SMAAT Artificial Intelligence systems. To process large volumes of documents and, as in the case of this post, to detect signatures.
The first thing is to clearly understand the difference between detecting and validating signatures. In the first case, it is a matter of knowing whether or not there is a signature in a particular document; if there is any type of stroke that resembles what we understand to be a signature. However, when we talk about validating a signature, we are referring to checking one signature with another and, as such, confirming whether it is the same. This is one way of, among other things, avoiding fraud and forged documents.
At Serimag we are experts in detection, i.e. in distinguishing a document that is correctly signed from one which is not. If it is an invalid scrawl or merely a mark which would not be valid either. It involves mass detection of signatures, something which is not possible in the case of validation. Drawing on Machine Learning we are able to find out whether the documents are signed or not and, if so, whether the signature can be accepted as such. We are not talking about whether it is authentic or a forgery, but simply whether or not it is a signature that can be understood as such.
As you can see, Machine Learning offers interesting competitive advantages to businesses. It is a discipline of artificial intelligence that focuses on reinforcing the learning capacity of computer systems, in this case for signature detection. This has a very positive effect on data processing, giving more power to automation and, of course, reliability. Don’t forget that we are talking about cutting-edge technology with a very low margin of error.