Handwritten character recognition combining Random Forest kernel and multiclass SVM

Fan Zhou

doi:10.1117/12.2641508

10 November 2022 Handwritten character recognition combining Random Forest kernel and multiclass SVM

Fan Zhou

Proceedings Volume 12348, 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022); 123480V (2022) https://doi.org/10.1117/12.2641508
Event: 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022), 2022, Zhuhai, China

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 algorithms’ 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 𝟑𝟐× 𝟑𝟐. 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.

Citation Download Citation

Fan Zhou "Handwritten character recognition combining Random Forest kernel and multiclass SVM", Proc. SPIE 12348, 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022), 123480V (10 November 2022); https://doi.org/10.1117/12.2641508

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KEYWORDS

Optical character recognition

Performance modeling

Data modeling

Binary data

Detection and tracking algorithms

Visualization

Fluctuations and noise

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