Case Study: Fine Writing Style Classification Using Siamese Neural Network

Alaa Abdalhaleem, Berat Kurar Barakat, Jihad El-Sana

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

10 Scopus citations

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.

Original languageEnglish
Title of host publication2nd IEEE International Workshop on Arabic and Derived Script Analysis and Recognition, ASAR 2018
PublisherInstitute of Electrical and Electronics Engineers
Pages62-66
Number of pages5
ISBN (Electronic)9781538614594
DOIs
StatePublished - 2 Oct 2018
Event2nd IEEE International Workshop on Arabic and Derived Script Analysis and Recognition, ASAR 2018 - London, United Kingdom
Duration: 12 Mar 201814 Mar 2018

Publication series

Name2nd IEEE International Workshop on Arabic and Derived Script Analysis and Recognition, ASAR 2018

Conference

Conference2nd IEEE International Workshop on Arabic and Derived Script Analysis and Recognition, ASAR 2018
Country/TerritoryUnited Kingdom
CityLondon
Period12/03/1814/03/18

Keywords

  • Deep-learning
  • Siamese-network
  • Supervised-learning
  • Writer-identification
  • Writing-style

ASJC Scopus subject areas

  • Signal Processing
  • Linguistics and Language
  • Computer Vision and Pattern Recognition

Fingerprint

Dive into the research topics of 'Case Study: Fine Writing Style Classification Using Siamese Neural Network'. Together they form a unique fingerprint.

Cite this