Document Clustering Based on Text and Image Content for Improving the Automatic Creation of Structural Descriptions in the Stream Input System

Student: Sverbiagin Nikita

Educational Programme: Applied Mathematics and Information Science (Bachelor)

Year of Graduation: 2020

A lot of companies today face a massive number of documents. It is nearly impossible to process these documents by humans, so there is a need for automatic document processing tools. One aspect of processing is the extraction of information. There is already a tool in our company that builds structural descriptions for documents, and with these descriptions, it is possible to extract data in automatic mode. We suggest an improvement to an existing tool by splitting documents by their structure so that the current method can be more precise.

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