Document classification, as its name implies, categorizes documents based on their content to relevant and appropriate classes or categories. Classifying the content of the documents relies mostly on how these documents are needed for an institution’s operations.
For instance, a business can classify documents by separating files for customer information, sales, products, invoices, receipts, and more. Note that document classification starts with identifying the text in the document, then tags the text and classifies the document based on the insights obtained from text classification.
Document classification can be manual or automated. With the advancement of technology, most businesses and institutions are using automated document classification.
Automated document classification uses machine learning and natural language processing systems to categorize documents. These tools make document classification quicker, more scalable, and less human bias.
Moreover, there are three levels of document classification. The first level classifies the document depending on the file format, such as jpeg, png, pdf, jiff, and more.
The second level is based on document structure. Structured documents have fixed layouts and tables for data (e.g. tax forms), while semi-structured documents have no fixed templates but with key-value pairs (e.g. invoices), and unstructured documents have no structures at all (e.g. contracts).
Lastly, the third level is based on document type, which has pre-processing, tagged data set, and classification methods as respective categories.