Lexicon-free handwritten word spotting using character HMMs
Pattern Recognition Letters
Combining neural networks to improve performance of handwritten keyword spotting
MCS'10 Proceedings of the 9th international conference on Multiple Classifier Systems
Writer identification in handwritten musical scores with bags of notes
Pattern Recognition
Fisher kernel based relevance feedback for multimodal video retrieval
Proceedings of the 3rd ACM conference on International conference on multimedia retrieval
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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.