On-line writer identification method based on FIR system characterizing pen-tip movement

Matsuura, T.; Thumwarin, P.

doi:10.1109/icses.2008.4673392

Cited by 5 publications

(2 citation statements)

References 5 publications

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“…Since our model represents an advancement to online writer identification, we situate our review in the online paradigm. A signal-processing approach is used by Matsuura and Thumwarin [10] where a finite impulse response (FIR) system is modelled by using the discrete cosine transform (DCT) of the pen-tip coordinates. Identification is accomplished by comparing the impulse response pattern of the test data with the referenced FIR system [10].…”

Section: Extant Research In Online Methodsmentioning

confidence: 99%

“…A signal-processing approach is used by Matsuura and Thumwarin [10] where a finite impulse response (FIR) system is modelled by using the discrete cosine transform (DCT) of the pen-tip coordinates. Identification is accomplished by comparing the impulse response pattern of the test data with the referenced FIR system [10]. Li and Tan [11] propose a text-independent system which uses a codebook of features based on temporal sequence and shape codes related to the writing speed, pressure and trajectory.…”

Section: Extant Research In Online Methodsmentioning

confidence: 99%

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A hierarchical Bayesian approach to online writer identification

Shivram¹,

Ramaiah²,

Govindaraju³

2013

IET biom.

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With the explosive growth of the tablet form factor and greater availability of pen-based direct input, online writer identification is increasingly becoming critical for person identification, digital forensics as well as downstream applications such as intelligent and adaptive user environments, search, indexing and retrieval of handwritten documents. Extant research has approached writer identification by using writing styles as a discriminative function between writers. In contrast, the authors model writing styles as a shared component of an individual's handwriting. They develop a theoretical framework for this conceptualisation and model it by using a three-level hierarchical Bayesian model (Latent Dirichlet Allocation). In this text-independent, unsupervised model each writer's handwriting is modelled as a distribution over finite writing styles that are shared among writers. They test their model on a new online handwriting dataset IBM_UB_1 and also offer benchmark comparisons by using the IAM-OnDB database. Their experiments show comparable results to the current benchmarks and demonstrate the efficacy of explicitly modelling the shared writing styles.

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Section: Extant Research In Online Methodsmentioning

confidence: 99%

Section: Extant Research In Online Methodsmentioning

confidence: 99%

A hierarchical Bayesian approach to online writer identification

Shivram¹,

Ramaiah²,

Govindaraju³

2013

IET biom.

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Semi‐supervised framework for writer identification using structural learning

Porwal¹,

Govindaraju²

2013

IET biom.

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Writer identification is a complex task as the handwriting of an individual encapsulates lot of information pertaining to text and personality of a writer. To learn a model to distinguish one writer from the other, it is important to capture every nuance of the handwriting of an individual. Learning such model poses two challenges. First, discriminatory variables maybe large and potentially related leading to a complex discriminatory function. Second, it will require large amount of training data to learn a complex and possibly high-dimensional function. In this study, the authors are proposing a semi-supervised framework for writer identification for offline handwritten documents that leverages the information hidden in the unlabelled samples. Proposed framework models the complexity of approximating the optimal hypothesis by breaking the main task into several subtasks and learning a separate hypothesis for each subtask. All the hypotheses pertaining to the subtasks will be used for the best model selection by retrieving a common substructure that has high correspondence with all the candidate hypotheses. The obtained substructure acts as a knowledge base that has the contextual information, which is otherwise difficult to retrieve. The extra information can be used to improve the performance of the identification model.

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On-line Thai numeral recognition

Matsuura

Tangtisanon²,

Thumwarin³

IEEE APCCAS 2000. 2000 IEEE Asia-Pacific Conference on Circuits and Systems. Electronic Communication Systems. (Cat. No.00EX394

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On-line writer identification method based on FIR system characterizing pen-tip movement

Cited by 5 publications

References 5 publications

A hierarchical Bayesian approach to online writer identification

A hierarchical Bayesian approach to online writer identification

Semi‐supervised framework for writer identification using structural learning

On-line Thai numeral recognition

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