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- W4380451233 abstract "In this study, handwritten Hindi characters are recognized using a convolutional neural network that is CNN-based technique. After being recognized, the characters can be used in a variety of ways and stored digitally on your computer. The characters in these pictures are all written in the Devanagari script. Every one of the 46-character classes has 2000 patterns. The training set makes up 85% of the data set, whereas the test set makes up 15%. OCR systems’ classification algorithms can be tested using image data sets. The highest Curacy score in the test set was 98.47. For the development and testing of handwritten text recognition systems, it offers a sizable collection of Devanagari handwriting styles created by numerous authors. Three completely connected detection layers are added after his four CNN layers. The input takes the form of a handwritten image in grayscale. Utilize filters to extract unique information from each layer in your photos. Convolution is used to achieve this. The processes of bunching and flattening are also crucial. A fully linked layer receives the output of the CNN layer and processes it. The character with the highest score is shown as the outcome after computing the chance or probability value for each character. There are 98.94 curates for acknowledgment. For this goal, there are already models that are similar, but the new model was more effective and precise than some of the earlier versions." @default.
- W4380451233 created "2023-06-14" @default.
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- W4380451233 date "2023-01-01" @default.
- W4380451233 modified "2023-09-25" @default.
- W4380451233 title "Online Handwriting Recognition in Multiple Languages" @default.
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- W4380451233 doi "https://doi.org/10.1007/978-981-99-0769-4_23" @default.
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