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- W4308703443 abstract "Handwritten character recognition can be applied in education, finance, social good, and many other fields. Varieties of kernelized methods are applied to this field. However, the fact is that many relative kernelized algorithmsโ performances depend greatly on the kernels, and the number of current choices is quite limited. This paper uses Random Forest to generate a kernel and constructs a multiclass SVM classifier based on the kernel for handwritten character recognition. The proposed method can generate a targeted kernel by analyzing the dataset, which improves the prediction accuracy of the final model. At first, all the images are compressed by grayscale transformation, binary conversion, and redundancy elimination, and their sizes are uniform to ๐๐× ๐๐. Then, the data selected for training and validation is used to build a Random Forest model responsible for generating the Random Forest kernel. Finally, based on the kernel constructed before, multiclass SVM is used to classify the dataset into 36 kinds of handwritten characters. The proposed method is compared with a Linear-kernel SVM model, a Polynomial-kernel SVM model, and a Random Forest model to evaluate the model's efficiency. The experiment results demonstrate that SVM with Random Forest kernel can achieve relatively high accuracy while recognizing handwritten characters." @default.
- W4308703443 created "2022-11-14" @default.
- W4308703443 creator A5064031029 @default.
- W4308703443 date "2022-11-11" @default.
- W4308703443 modified "2023-09-25" @default.
- W4308703443 title "Handwritten character recognition combining Random Forest kernel and multiclass SVM" @default.
- W4308703443 cites W1967903496 @default.
- W4308703443 cites W2030276403 @default.
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- W4308703443 doi "https://doi.org/10.1117/12.2641508" @default.
- W4308703443 hasPublicationYear "2022" @default.
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