A Comprehensive Analysis of Deep Learning Techniques for Documentation Classification

The continuously increasing volume of documents in different fields has rendered document classification by manual labor infeasible. This has led to the genesis of automatic classification with the help of a myriad of techniques like Data Mining, Machine learning, and Deep Learning (N.L.P). This automation prevents human error and, more importantly, builds up speed, which is critical given the volubility of the data. Document classification has seen many developments in the past decade. This paper presents a comprehensive analysis of various state-of-the-art deep-learning algorithms for document classification and perform extensive experiments on these techniques. The performance of these algorithms is analyzed quantitatively using established metrics.