Case Study: Fine Writing Style Classification Using Siamese Neural Network

Abstract

This paper presents an automatic system for dividing a manuscript into similar parts, according to their similarity in writing style. This system is based on Siamese neural network, which consists of two identical sub-networks joined at their outputs. In the training the two sub-networks extract features from two patches, while the joining neuron measures the distance between the two feature vectors. Patches from the same page are considered as identical and patches from different books are considered as different. Based on that, the Siamese network computes the distances between patches of the same book.