Machine Learning approach for Multi-Page Document Classification

In current technologically advanced era, everything is digitized, or tend to be digital. Segregating documents into different groups and classifying them based on the content similarity and context correlations is a challenging task. Document classification, studied as a part of information retrieval, finds its application with efficient data organization, fruitful search for information and redundant data management.The proposed work - Machine Learning approach for Multi Page Document Classification (ML-MPDC), uses machine learning approach to efficiently analyse multi-page document, and predict to which category the document belongs to. The proposed model uses supervised learning approach, and employs text vectorization algorithms such as Doc2Vec and regression algorithms such as linear regression to predict the document classes. The MLMPDC project has been skillfully designed and executed, leveraging machine learning algorithms to automate document sorting based on their respective classes. This implementation significantly benefits businesses by reducing workforce costs and minimizing the time required for document sorting. The model’s performance substantiates its effectiveness in achieving these objectives.