Improved Handwriting Recognition by Combining Two Forms of Hidden Markov Models and a Recurrent Neural Network

Abstract : Handwritten word recognition has received a substantial amount of attention in the past. Neural Networks as well as discriminatively trained Maximum Margin Hidden Markov Models have emerged as cutting-edge alternatives to the commonly used Hidden Markov Models. In this paper, we analyze the combination of these classifiers with respect to their potential for improving recognition performance. It is shown that a significant improvement can in fact be achieved, although the individual recognizers are highly optimized state-of-the-art systems. Also, it is demonstrated that the diversity of the recognizers has a profound impact on the improvement that can be achieved by the combination.
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Conference papers
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https://hal.archives-ouvertes.fr/hal-01297958
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Submitted on : Tuesday, April 5, 2016 - 11:00:17 AM
Last modification on : Thursday, September 19, 2019 - 2:20:04 PM

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Volkmar Frinken, Tim Peter, Andreas Fischer, Horst Bunke, Trinh Minh Tri Do, et al.. Improved Handwriting Recognition by Combining Two Forms of Hidden Markov Models and a Recurrent Neural Network. International Conference on Computer Analysis of Images and Patterns (CAIP), Sep 2009, Münster, Germany. pp.189-196, ⟨10.1007/978-3-642-03767-2_23⟩. ⟨hal-01297958⟩

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