Automatic Document Classification

Document Classification

The most important input of business decision support systems is information. Today, we know that most of the information is based on documents. These are in the form of documents. According to Wikipedia
According to the Turkish Language Association, the correct spelling of the word “document” is “document”. In order to structurally capture this information, it first needs to be classified. This is where the concept of document classification comes into play.

As the number and types of documents increase, they become harder to manage. So much so that they need to be categorized. But in large volumes this is almost impossible to do by human hands. This is where the concept of automatic document classification comes in. An automated document classification system not only helps us to save information, but also to find these documents when needed.

What is Document Classification?

Document classification, as the name suggests, is the process of sorting documents into relevant categories or classes. This makes the process of organizing and protecting documents and data easy and efficient. Therefore, in such cases, automatic document classification methods are needed.

The other most common example in the classification field is email classification. Classifications by folder or spam or not spam are also examples.

How to Classify Documents?

There are two methods of document classification: manual and automatic classification. In manual classification, the interpretation of the classification criteria is done by a human. In large volumes this requires considerable effort.

Automatic document classification, on the other hand, makes use of artificial intelligence techniques. This process is much faster, more scalable, accurate and cost-effective compared to manual classification.

Techniques Used in Automatic Document Classification

  1. Supervised Learning: In this method, the system learns from examples that have both inputs and corresponding classes or outputs. The algorithm is trained on a set of manually labeled documents. Once training is complete, the classifier predicts categories based on a confidence interval.
  2. Unsupervised Learning: In this approach, similar documents are grouped into different clusters without any pre-training. This classification includes templates, font words or tags, etc. can be done on a case-by-case basis. These algorithms can achieve higher accuracy if certain rules are defined and fine-tuned.
  3. Rule-based The rule-based technique is one of the traditional document classification methods that takes advantage of a system’s capacity to understand natural language and write grammatical rules to instruct the system to behave like a human when classifying a document. This method has the advantage of improving performance on a regular basis, rather than relying solely on statistics or math like the previous two methods. This method is associated with higher accuracy, especially in complex scenarios. However, building a state-of-the-art model based on rules is time-consuming and difficult to scale.
  4. Hybrid: Applied to optimize time and at the same time increase classification success. Supervised learning and rule-based method work together.

Papirus AI Automatic Document Classification Service

Papirus AI has its own hybrid model classification infrastructure. It only supports the constraints of using AI with a rule-based model. It achieves the highest classification success in the shortest time. Moreover, it carries out all this preparation process on behalf of its customers. You only need to evaluate the results.

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