Abstract
In this work we present a framework for recognizing writer for a handwritten word. We make use of allographic features at sub-word level. Our work is motivated by previous techniques which make use of a codebook. However, instead of encoding the features using the codewords, we exploit the discriminative properties of features that belong to the same cluster, in a supervised approach. We are able to achieve writer identification rates close to 63% on the handwritten words drawn from a dataset by 10 writers. Our work has application in scenarios where multiple writers write/annotate on the same page.
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Pandey, S., Harit, G. (2017). Writer Identification for Handwritten Words. In: Mukherjee, S., et al. Computer Vision, Graphics, and Image Processing. ICVGIP 2016. Lecture Notes in Computer Science(), vol 10481. Springer, Cham. https://doi.org/10.1007/978-3-319-68124-5_23
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DOI: https://doi.org/10.1007/978-3-319-68124-5_23
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