On-line handwritten text categorization

Saldarriaga, Sebastián Peña; Viard-Gaudin, Christian; Morin, Emmanuel

doi:10.1117/12.804355

Cited by 5 publications

(3 citation statements)

References 15 publications

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“…The handwritten collection used in our experiments was previously used in text categorization experiments. 11 An obvious difference between TC and IR test collections is that TC collections do not have a standard set of queries with their corresponding relevant documents. However, documents are labeled with category codes.…”

Section: From a Text Categorization To An Ir Collectionmentioning

confidence: 99%

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Combining approaches to on-line handwriting information retrieval

Saldarriaga¹,

Viard-Gaudin

Morin³

2010

Document Recognition and Retrieval XVII

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In this work, we propose to combine two quite different approaches for retrieving handwritten documents. Our hypothesis is that different retrieval algorithms should retrieve different sets of documents for the same query. Therefore, significant improvements in retrieval performances can be expected. The first approach is based on information retrieval techniques carried out on the noisy texts obtained through handwriting recognition, while the second approach is recognition-free using a word spotting algorithm. Results shows that for texts having a word error rate (WER) lower than 23%, the performances obtained with the combined system are close to the performances obtained on clean digital texts. In addition, for poorly recognized texts (WER > 52%), an improvement of nearly 17% can be observed with respect to the best available baseline method.

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Section: From a Text Categorization To An Ir Collectionmentioning

confidence: 99%

“…A corpus of more than 2,000 on-line documents is used for experimental validation. Since this corpus was initially collected for text categorization (TC), 11 it was adapted to fit the needs of our IR experiments (see Section 3).…”

Section: Introductionmentioning

confidence: 99%

Combining approaches to on-line handwriting information retrieval

Saldarriaga¹,

Viard-Gaudin

Morin³

2010

Document Recognition and Retrieval XVII

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“…Online handwritten documents were collected from 200 writers where the writers copied text passages from the Reuters financial news corpus, Reuters-21578 [Saldarriaga, Viard-Gaudin et al 2009]. Figure 4-1 illustrates a typical online handwritten sample that was collected.…”

Section: Methodsmentioning

confidence: 99%

Writing style modelling based on grapheme distributions : application to on-line writer identification.

Tan¹

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Teach a man to fish and he'll feed himself for a lifetime" This is the philosophy of my supervisors, Professor Alex Kot Chichung and Professor Christian Viard-Gaudin who taught me the ways and means to acquire knowledge on my own instead of simply transferring them to me directly. I will like to take this opportunity to express my heartfelt gratitude to the both of them for their patience and understanding. I am truly grateful to them for their sharp perspectives and insightful criticisms. I also wish to take this chance to thank my previous final year project mentor, Dr. Kantisara Pita (Associate Professor) for his teachings that had equipped me to take on many of the challenges encountered over my PhD studies. This self-discovery journey was also made possible by the following parties; NTU and the French embassy for having this brilliant program and awarding me with the

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Individuality of alphabet knowledge in online writer identification

Tan

Viard-Gaudin

Kot

2010

IJDAR

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International audienceCharacter prototype approaches for writer identification produces a consistent set of templates that are used to model the handwriting styles of writers, thereby allowing high accuracies to be attained. This paper extends such work on writer identification by investigating the usage of alphabet knowledge derived from the character prototypes. In addition, we demonstrate the concept of discriminative power of alphabets. It is not unconceivable that certain alphabets allow writers to express their individuality of handwriting with a more distinct and unique style compared with other alphabets. This paper establishes that such alphabets have higher discriminative powers in identifying writers. Experiments related to the reduction in dimensionality of the writer identification system are also reported. Our results show that the discriminative power of alphabet can be used to reduce the complexity while maintaining the same level of performance for the writer identification system

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On-line handwritten text categorization

Cited by 5 publications

References 15 publications

Combining approaches to on-line handwriting information retrieval

Combining approaches to on-line handwriting information retrieval

Writing style modelling based on grapheme distributions : application to on-line writer identification.

Individuality of alphabet knowledge in online writer identification

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