Myint San

Work place: Faculty of Information Science, University of Computer Studies (Monywa), Myanmar



Research Interests: Data Structures and Algorithms, Data Mining, Computer Architecture and Organization


Myint San: Lecturer of Faculty of Information Science in Computer University (Monywa), Myanmar. He received his B.C.Sc degree in 2003, and B.C.Sc(Hons;) in 2004 from University of Computer studies (Monywa), Myanmar. He graduated for M.C.Sc degree in 2005 from University of Computer Studies, Mandalay, Myanmar. His research interest includes image processing, and data mining.

Author Articles
Study for License Plate Detection

By Mie Mie Aung Phyu Phyu Khaing Myint San

DOI:, Pub. Date: 8 Dec. 2019

License Plate Detection (LPD) system is the application of computer vision and image processing technology. LPD system is the first and main step of License Plate Recognition (LPR) system. So, it performs as the main driver of the LPR system. License plate detection step is always performed in front of the license plate recognition step. LPD system takes the vehicle images as input, follows with the general steps: such as reprocessing, localization, region extraction, and region detection, and the detected image are the output of the system. There are many algorithms for LPD while detecting a license plate in different conditions is still a complex task. For the LPD system, morphological operation and deep learning model are mostly used. This paper presents the critical study of the license plate detection system and also examines the implementation of new technologies of the license plate detection system.

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Fruit Recognition Using Color and Morphological Features Fusion

By Myint San Mie Mie Aung Phyu Phyu Khaing

DOI:, Pub. Date: 8 Oct. 2019

It is still difficult to recognize the kind of fruit which are of different colors, shapes, and textures. This paper proposes a features fusion method to recognize five different classes of fruits that are the images from the fruit360 dataset. We are processed with four stages: pre-processing, boundary extraction, feature extractions, and classification. Pre-processing is performed to remove the noise by using the median filter, and boundary extraction are operated with the morphological operation. In feature extraction, we have extracted two types of features: color, and morphological features of the image. Color features are extracted from the RGB color channel, and morphological features are extracted from the image that detected the boundary of fruit by using morphological operations. These two types of features are combined in a single feature descriptor.  These features are passed to five different classifiers: Naïve Bayes (NB), Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighbor (KNN), and Random Forest (RF). In the study, the accuracy that classified with Random Forest (RF) classifier for the proposed feature fusion method is better than the other classifiers, such as Naïve Bayes (NB), Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighbor (KNN).

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