A study on idiosyncratic handwriting with impact on writer identification

Document Type

Conference Article

Publication Title

Proceedings of International Conference on Frontiers in Handwriting Recognition, ICFHR

Abstract

In this paper, we study handwriting idiosyncrasy in terms of its structural eccentricity. In this study, our approach is to find idiosyncratic handwritten text components and model the idiosyncrasy analysis task as a machine learning problem supervised by human cognition. We employ the Inception network for this purpose. The experiments are performed on two publicly available databases and an in-house database of Bengali offline handwritten samples. On these samples, subjective opinion scores of handwriting idiosyncrasy are collected from handwriting experts. We have analyzed the handwriting idiosyncrasy on this corpus which comprises the perceptive ground-truth opinion. We also investigate the effect of idiosyncratic text on writer identification by using the SqueezeNet. The performance of our system is promising.

First Page

193

Last Page

198

DOI

10.1109/ICFHR-2018.2018.00042

Publication Date

12-5-2018

Comments

Open Access, Green

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