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- W4304205482 abstract "Abstract Handwritten character recognition has always been an interesting area of pattern recognition for research in the field of image processing. Unlike its counterpart of English Character Recognition (CR), Devanagari Optical Character Recognition poses its own complications (DOCR). With 36 consonants, 14 vowels and 10 digits, it’s only generic that Nepali Devanagari Optical Character Recognition models tend to go bigger hence less efficient, but, as with all other problems, non efficient solutions are of no use to the real world. This paper includes novel research on accurate yet efficient Convolutional Neural Network (CNN) architecture for DOCR. Dataset from Devanagari Handwritten Character Dataset (DHCD) was used to train the model to result in the highest accuracy achieved on the dataset along with ensured efficiency." @default.
- W4304205482 created "2022-10-11" @default.
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- W4304205482 date "2022-10-11" @default.
- W4304205482 modified "2023-10-16" @default.
- W4304205482 title "Predicting Handwritten Devanagari Characters using modified-Lenet Model Architecture" @default.
- W4304205482 doi "https://doi.org/10.21203/rs.3.rs-2137648/v1" @default.
- W4304205482 hasPublicationYear "2022" @default.
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