Combining neural networks to improve performance of handwritten keyword spotting

Authors:
Volkmar Frinken;Andreas Fischer;Horst Bunke
Affiliations:
Institute of Computer Science and Applied Mathematics, University of Bern, Bern, Switzerland;Institute of Computer Science and Applied Mathematics, University of Bern, Bern, Switzerland;Institute of Computer Science and Applied Mathematics, University of Bern, Bern, Switzerland
Venue:
MCS'10 Proceedings of the 9th international conference on Multiple Classifier Systems
Year:
2010

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Abstract

Keyword spotting refers to the process of retrieving all instances of a given word in a document. It has received significant amounts of attention recently as an attractive alternative to full text transcription, and is particularly suited for tasks such as document searching and browsing. In the present paper we propose a combination of several keyword spotting systems for unconstrained handwritten text. The individual systems are based on a novel type of neural network. Due to their random initialization, a great variety in performance is observed among the neural networks. We demonstrate that by using a combination of several networks the best individual system can be outperformed.