Abstract
In this article, we propose an original hybrid CRF-HMM system for handwriting recognition. The main idea is to benefit from both the CRF discriminative ability and the HMM modeling ability. The CRF stage is devoted to the discrimination of low level frame representations, while the HMM performs a lexicon-driven word recognition. Low level frame representations are defined using \(n\)-gram codebooks and HOG descriptors. The system is trained and tested on the public handwritten word database RIMES.
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Bideault, G., Mioulet, L., Chatelain, C., Paquet, T. (2014). A Hybrid CRF/HMM Approach for Handwriting Recognition. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2014. Lecture Notes in Computer Science(), vol 8814. Springer, Cham. https://doi.org/10.1007/978-3-319-11758-4_44
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DOI: https://doi.org/10.1007/978-3-319-11758-4_44
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