SVM, KNN, Random Forest, and Neural Network based Handwritten Nepali Barnamala Recognition

Abstract

Nepali Barnamala consists 36 consonants, 12 vowels and 10 Nepali digits. Among them, this paper uses the 36 consonants and 10 Nepali digits for the recognition using machine learning based algorithm mainly: Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Random Forest (RF) and several architectures of neural networks. In this paper, different kernel tricks of SVM with different regularization parameters has been used to train model and has compared their accuracy and F1-score. In KNN, accuracy and F1-score are compared with different values of K and distance matric. In Neural Networks, training accuracy, training loss, validation accuracy, and validation loss are compared with different number of hidden layers regularization parameters and learning rate. Different hyperparameter of random forest are changed and compared to their corresponding result. This paper uses the Kaggle dataset of school students’ Nepali handwritten characters. The dataset is CSV format with 78,200 rows for forty-six different classes with 1024 (32*32 image size) columns plus one column for label of characters for training and 13,800 rows for testing. For handwritten Nepali Barnamala recognition, the best average accuracy is 93.51% of neural networks with four hidden layers.