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- W2946874795 abstract "With the increased demand, exploration and globalization of digitized Devanagari documents, many printed and handwritten mono-lingual character recognition techniques have evolved since last two decades. This paper presents an efficient Devanagari character classification model using SVM for printed and handwritten mono-lingual Hindi, Sanskrit and Marathi documents, which first preprocesses the image, segments it through projection profiles, removes shirorekha, extracts features, and then classifies the shirorekha-less characters into pre-defined character categories. The experiments performed on proposed system obtained average classification accuracies of 99.54% and 98.35% for printed and handwritten images, respectively, and showed better performance than other techniques." @default.
- W2946874795 created "2019-06-07" @default.
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- W2946874795 date "2019-01-01" @default.
- W2946874795 modified "2023-10-02" @default.
- W2946874795 title "An efficient Devanagari character classification in printed and handwritten documents using SVM" @default.
- W2946874795 cites W2894465708 @default.
- W2946874795 doi "https://doi.org/10.1016/j.procs.2019.05.033" @default.
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