An Empirical Study on Writer Identification & Verification from Intra-variable Individual Handwriting

TL;DR

This study investigates whether certain features of handwriting are consistent enough to identify or verify writers despite high variability in handwriting styles, using Bengali offline handwriting data and machine learning models.

Contribution

It provides an empirical analysis of handcrafted and auto-derived features for writer identification and verification under high intra-variability conditions.

Findings

Handcrafted features with SVM achieved notable accuracy.

Auto-derived features from convolutional networks showed promising results.

Data augmentation improved model robustness.

Abstract

The handwriting of an individual may vary substantially with factors such as mood, time, space, writing speed, writing medium and tool, writing topic, etc. It becomes challenging to perform automated writer verification/identification on a particular set of handwritten patterns (e.g., speedy handwriting) of a person, especially when the system is trained using a different set of writing patterns (e.g., normal speed) of that same person. However, it would be interesting to experimentally analyze if there exists any implicit characteristic of individuality which is insensitive to high intra-variable handwriting. In this paper, we study some handcrafted features and auto-derived features extracted from intra-variable writing. Here, we work on writer identification/verification from offline Bengali handwriting of high intra-variability. To this end, we use various models mainly based on handcrafted features with SVM (Support Vector Machine) and features auto-derived by the convolutional network. For experimentation, we have generated two handwritten databases from two different sets of 100 writers and enlarged the dataset by a data-augmentation technique. We have obtained some interesting results.