Classification deals with the categorization of objects. In our process automation and digitization world, we often think of the objects as complete documents that need to be classified. Of course, it is important to understand what the type of a document is...

One of the frequently overlooked and really difficult problems in document automation, which is also really annoying in daily processing, is the automatic separation of a stack of documents into single meaningful documents and assignment to a document class. The goal would be to simply scan the whole stack and have it separated by an intelligent algorithm. Fortunately this is readily available today from the Skilja technology stack as a built in feature into the Laera classifier. This does not say it is easy. It requires quite some experience and infrastructure to manage several interdependent steps of classification and separation in a stable and reliable way. This is what Laera provides out of the box.

Understanding the quality of an automatic classification system is crucial for its acceptance and any attempt to improve it over time. Quality means that we need to look at errors and at the recognition rate. In classification terms these values are...

This is the fourth and final post on the characteristics of a good content based classifier. In previous posts we have focused on presentation of statistical results and comparison of the Skilja Classifier to a plain vanilla naïve Bayes classifier in Recall-Precision...

In recent articles about classifier quality we have focused on the overall statistical results. For this we have used either the precision-recall graph or the inverted precision graph. While these are very good tools to predict the overall quality of a classification...

In our small series about classification quality we have used the precision-recall graph to show the difference between a very good and a so-so classifier in a recent post that you can find here. This graphical representation is very common and easy to understand....

Auto-Classification is able to assign categories and hence meaning to documents with an unprecedented speed and quality. The technology for auto-classification has been developed over the last 15 years – from the first tentative rule based systems to elaborate...

In principle it is possible to train enough representative samples to create a classification scheme that is totally generic for a specific purpose. This is what humans do all the time. Reading a text and correctly classifying it manually into a given...

Classification  tries to mimic human understanding. Several methods have been developed in the past to achieve what we as humans can do almost effortless.  These methods can be divided into two groups. Rule based classification Rule based systems are...

For an active production system, but also when the classification scheme is set up, it is very important to measure the quality of the classification. The goal is to create as few as possible errors in classification (also called false positives) as these can severely...