Using Deep Neural Nets in Writer Identification & Analysis

<p>This thesis focuses on developing automated deep learning methods for writer identification and writer attribute prediction from handwriting. It introduces two novel architectures: Convolutional Transformer Encoder (CTE) and Convolutional Swin Encoder (CSE). CTE is designed for determining authorship from handwritten text, be it modern or historical handwriting. CTE tracks subtle features and cues from handwriting strokes to distinguish authorship. It is the first of its kind capable to operate on historical handwritten fragments, setting it apart from existing methods that rely on entire document pages. CSE is designed to determine multiple attributes of an author such as authorship, gender, age and handedness. It achieves competitive performance compared to traditional page-level methods that typically rely on separate classifiers. While CTE is an exclusive classifier using a Transformer block, CSE is a multi-label classifier leveraging Swin Transformer blocks.</p>

Name thesis:degree_name

Master of Science (M.S.)

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Computer Science

Year dc:date.available

2025

Author dc:creator

• Majithia, Aditya

Contributors dc:contributor

• Michael Grossberg

• Deep Learning
• Writer identification
• Writer attributes
• Individual word images
• Historical fragments analysis
• Multi-label classification.
• Data Science

Repository record dc:identifier

https://academicworks.cuny.edu/cc_etds_theses/1270

OAI identifier oai:identifier

oai:academicworks.cuny.edu:cc_etds_theses-2330