Image Feature Extraction of Numbers and Letters Using Matrix Segmentation

Abstract

Classification in recognizing image of letters and numbers is useful to recognize vehicle license plates. This study aims to maximize classification accuracy value of feature extraction method using matrix segmentation. The dataset consists of 300 vehicle license plate images which have 36 classifications, 26 classes for A-Z letters image, and 10 classes for 0-9 numbers image. The research stages carried out to maximize the results of the classification using multiclass SVM-RBF nonlinear are: preparing region image of interest, image enhancement, image segmentation of letters and numbers, determining the best n value for n x n matrix segmentation, calculating total points of each segment as feature value, and determining the best value for C and γ as the value of RBF kernel parameter. The result of this study shows a maximum value of 92% classification accuracy using n = 5, γ = 0.8, and C = 15.

Non-linear Multiclass SVM Classification Optimization using Large Datasets of Geometric Motif Image

“…Maximum accuracy value of image classification depends on whether the value of generated image features is good or not. Thus, the experiment on applying the right feature extraction method can be an interesting stage in the initial image recognition research [1][2][19][20]. Feature extraction methods are commonly divided into three, i.e.…”

“…These classification algorithms are categorized as Shallow Learning type since they still require some application of feature extraction algorithms to produce feature image dataset. Feature extraction is a fundamental part in classification as feature dataset obtained from proper feature extraction can maximize the accuracy value of classification results [1][2].…”

“…The advantages of classification using SVM are such as the ability to significantly gain good classification accuracy values for image features with high data dimensions [3,4]. However, it is still necessary to test the classification with several different kinds of feature dimensions to get the right dimensions for use [1][2]. The advantages of SVM than those of Artificial Neural Network (ANN) in classification are that SVM does not involve all vector dataset for training image features in forming hyperplane and margins as class separators, and only vector datasets for contributing image features (support vector) are used for hyperplane formation and margins.…”

“…To solve such weaknesses of non-linear multiclass SVM classification on large-scale image recognition, there are three experimental stages which can be carried out to optimize the use of SVM method in non-linear multiclass www.ijacsa.thesai.org classification, so that it may maximize classification accuracy value. The first stage requires an experiment focusing on the use of qualified feature extraction by determining feature extraction algorithm and its proper parameters to gain qualified dimensions and feature values [1][2]13], the second is conducting an experiment to determine the best kernel and its parameter values which are fit with the conditions of the dataset used [4,5,7,14], The classification of each new dataset with SVM depends on the kernel function used and the parameters used. And the third requires an experiment on using the right method to handle the training and testing process in multi-class.…”

“…In obtaining maximum classification accuracy value using SVM, it requires the best results from the three experiments by using the new image dataset. The author has conducted the first and the second experiments for 4 non-linear classes using oneagainst-all method for geometric pattern image dataset with a high level of feature similarity [1][2]5]. The dataset used Batik images with traditional motifs from Indonesia.…”