Fisher kernels for handwritten word-spotting

Research output: Book chapterConference contributionpeer-review

35 Citations (Scopus)

Abstract

The Fisher kernel is a generic framework which combines the benefits of generative and discriminative approaches to pattern classification. In this contribution, we propose to apply this framework to handwritten word-spotting. Given a word image and a keyword generative model, the idea is to generate a vector which describes how the parameters of the keyword model should be modified to best fit the word image. This vector can then be used as the input of a discriminative classifier. We compare the performance of the proposed approach with that of a generative baseline on a challenging real-world dataset of customer letters. When the kernel used by the classifier is linear, the performance improvement is marginal but the proposed system is approximately 15 times faster than the baseline. If we use a non-linear kernel devised for this task, we obtain a 15% relative reduction of the error but the detector is approximately 15 times slower.

Original languageEnglish
Title of host publicationICDAR2009 - 10th International Conference on Document Analysis and Recognition
Pages106-110
Number of pages5
DOIs
Publication statusPublished - 2009
Externally publishedYes
EventICDAR2009 - 10th International Conference on Document Analysis and Recognition - Barcelona, Spain
Duration: 26 Jul 200929 Jul 2009

Publication series

NameProceedings of the International Conference on Document Analysis and Recognition, ICDAR
ISSN (Print)1520-5363

Conference

ConferenceICDAR2009 - 10th International Conference on Document Analysis and Recognition
Country/TerritorySpain
CityBarcelona
Period26/07/0929/07/09

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