Individuality of Handwriting: A Validation Study
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
The Journal of Machine Learning Research
The author-topic model for authors and documents
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
A Statistical Model For Writer Verification
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Text-Independent Writer Identification and Verification Using Textural and Allographic Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
| Hi-index | 0.00 |
In this paper, we describe a novel approach to Writer Identification in Offline handwriting using Latent Dirichlet Allocation. State-of-the-art methods for writer identification employ the traditional feature-classification paradigm which does not provide enough information about the handwriting attributes such as writing style which are key components in any forensic analysis of handwriting. This problem is also compounded due to lack of efficient rules for defining a particular writing style that can capture writer specific characteristics over a large dataset. We propose to address this issue by using a generative model in form of Latent Dirichlet Allocation(LDA) that automatically infers writing styles from handwritten document collection without any pre-defined set of rules. This information is then used to represent each writer as a distribution over multiple writing style for classifying any unknown writer sample. We describe our approach on two different feature sets consisting of contour angle features as well as structural and concavity features. Our experimental results show comparable performance with baseline systems and also demonstrate the efficacy of LDA for learning multiple handwriting styles.