Ever wish you could find your documents faster, and then share them with people easier? It’s hard to find that one document in your cluttered, messy office. You know it exists somewhere — but where? Document classification is often used in a company to help you find the document you need more quickly.
Document classification is a process that involves assigning a document to one or more categories depending on its content. This process can be manual or automatic and can be done using a variety of techniques.
Manual classification is done by people, and it’s usually done by experts who have knowledge about the subject and know how to classify documents correctly. Automated classification, on the other hand, is performed by machines and it can be done in many ways – through optical character recognition or natural language processing for example.
According to a McKinsey report, employees spend 1.8 hours every day — 9.3 hours per week, on average — searching and gathering informationMcKinsey
In any document management strategy that organizations use, classification of documents is a crucial step. Without it, your company will suffer significant financial loss, and your staff will stop working efficiently.
Why do we need document classification?
Organizations can use document classification to organize their documents and protect their information. It can also help organizations with compliance and regulatory reporting.
The benefits of document classification for organizations are as follows:
1- Protecting sensitive or confidential data
2- Managing large volumes of data in a structured way
3- Ensuring that documents are properly classified according to the organization’s policies and procedures
4- Improving efficiency by reducing the time spent on searching for documents, sorting them, and filing them away
What is the difference between manual and automatic document classification?
Document classification is a process that determines the category of documents. The most common types of classification are manual and automatic. Each one has its own advantages and disadvantages.
Manual document classification is done by humans, with no real automation involved. In the past, this procedure was the only means to classify documents. When working with a large number of documents, it is highly difficult and error prone.
Automatic document classification uses a computer to automate the process, which can be done with or without human oversight. With the advancement of technologies, Machine Learning and AI capabilities gave us the possibilities to automatically identify the content of the document and tag them accordingly.
This process is much faster, more scalable, accurate, and cost-effective when compared with manual classification
How Automatic Document Classification Work?
In order to make an automatic document classification system work, you first need to have a list of keywords. These keywords are the ones that the system will use to classify the documents. The next step is to create a list of rules that will tell the system what criteria it should use when classifying documents. This can be done by writing a set of rules for each keyword and assigning them weights.
The next step is to train the system on a set of documents by feeding them into it and having it classify them according to how well its rules matched up with those in its training data. This will help you figure out where your weaknesses are and what kind of adjustments you need to make in order for your automatic document classification system work as efficiently as possible.
Here are some real-world example of how automatic classification of documents work
How Document Management Systems Ease the Document Classification Process?
The best document management software available in the market are usually equipped with advanced intelligent document processing engines to analyze and identify the document category as soon as it is available in the system.
Here is how they work
1- Identification of content
A document categorization engine will thoroughly examine the documents as soon as users begin importing them into their system and will provide recommendations for the best category.
In addition to identifying the content types, it is also capable of understanding different documents structures; Structured, Semi structured, and Unstructured documents.
Based on the structure, documents come in 3 categories:
1- Structured documents: They are a type of document that is designed to be easily understood by computers. They are designed in a way that all the content is arranged and organized in an easy-to-read format.
2- Semi structured documents: These documents are a form of document that has some structure and some flexibility. They have enough structure to be useful, but not too much so that they are overly rigid and difficult to use.
3- Unstructured documents: These are a new form of communication. It is a type of data that is not in any formal format, such as a spreadsheet or word document. Unstructured data is growing exponentially, and it is essential to understand the role it plays in the modern organizational context.
It is significant to highlight that this engine will continue to automatically learn about the various sorts of documents used in your company and improve itself.
2- Suggest categorization
Users will be able to view the outcomes and the automatic classification carried out by the engine once the procedure has been completed. They have the ability to alter it if they are dissatisfied.
The engine will make use of manual document tagging changes as a technique to improve going forward.
3- Continuous improvements
The auto classification engine is equipped with cognitive technologies to keep on improving because of its self-learning capabilities. With time, it will continue learning from previous transactions and understand more about your document types.
What is the different kind of document classification?
The two broad categories of document classification are:
1. Semantic classification
This type of document classification has been around for over 30 years and has been used to categorize documents as spam or not spam, as well as to identify topics and themes in collections of documents. The most popular algorithms for semantic classification are Latent Dirichlet Allocation (LDA) and Support Vector Machines (SVM).
Semantic classifiers use machine learning to analyze word frequencies from a collection of documents and categorize them into categories.
2. Statistical classification
It is based on the statistics of how different words are used in a document or a set of documents.
The process of document statistical classification is based on the assumption that words in a document are related to each other, and therefore, words in a particular category will be more likely to occur together than they would be if they were randomly distributed.