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- W2474855605 abstract "Statistical modelling techniques for automatic reading systems substantially rely on the availability of compact and meaningful feature representations. State-ofthe-art feature extraction for offline handwriting recognition is usually based on heuristic approaches that describe either basic geometric properties or statistical distributions of raw pixel values. Working well on average, still fundamental insights into the nature of handwriting are desired. In this paper we present a novel approach for the automatic extraction of appearance-based representations of offline handwriting data. Given the framework of deep belief networks – Restricted Boltzmann Machines – a two-stage method for feature learning and optimization is developed. Given two standard corpora of both Arabic and Roman handwriting data it is demonstrated across script boundaries, that automatically learned features achieve recognition results comparable to state-of-theart handcrafted features. Given these promising results the potential of feature learning for future reading systems is discussed." @default.
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- W2474855605 date "2011-01-12" @default.
- W2474855605 modified "2023-09-22" @default.
- W2474855605 title "Towards Feature Learning for HMM-based Offline Handwriting Recognition" @default.
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- W2474855605 doi "https://doi.org/10.17877/de290r-8202" @default.
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