Modeling Writing Styles for Online Writer Identification: A Hierarchical Bayesian Approach
| Autor: | Utkarsh Porwal, Arti Shivram, Venu Govindaraju, Chetan Ramaiah |
|---|---|
| Rok vydání: | 2012 |
| Předmět: |
Intelligent character recognition
Computer science business.industry Search engine indexing computer.software_genre Latent Dirichlet allocation Writing style symbols.namesake Discriminative model Handwriting Handwriting recognition symbols Unsupervised learning Artificial intelligence business computer Natural language processing |
| Zdroj: | ICFHR |
| Popis: | With the explosive growth of the tablet form factor and greater availability of pen-based direct input, writer identification in online environments is increasingly becoming critical for a variety of downstream applications such as intelligent and adaptive user environments, search, retrieval, indexing and digital forensics. Extant research has approached writer identification by using writing styles as a discriminative function between writers. In contrast, we model writing styles as a shared component of an individualâs handwriting. We develop a theoretical framework for this conceptualization and model this using a three level hierarchical Bayesian model (Latent Dirichlet Allocation). In this text-independent, unsupervised model each writerâs handwriting is modeled as a distribution over finite writing styles that are shared amongst writers. We test our model on a novel online/offline handwriting dataset IBM UB 1 which is being made available to the public. Our experiments show comparable results to current benchmarks and demonstrate the efficacy of explicitly modeling shared writing styles. |
| Databáze: | OpenAIRE |
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