2026-04-15T03:36:49Z https://meral.edu.mm/oai

oai:meral.edu.mm:recid/00007422 2021-12-13T02:10:17Z 1582963674932 1582963674932:1597397050488 user-miit

Myanmar Handwritten Digit and Character Recognition Using Blocked Features and Random Forest Classifier Myint San Phyu Phyu Khaing Moe Thida Naing Automatic recognition of handwritten digits and handwritten characters has been studied in the pattern recognition field for many years. The handwritten digits and character recognition are still a significant field of study, due to its wide practical applications. There has been much work in the field of Myanmar Optical Character Recognition (OCR) in recent decades. This study prepares the handwritten digit and character recognition dataset to train the model. The images in the dataset were arranged with black and white color space of 36 x 36 pixels standardized in size. This research proposed the Myanmar handwritten digit and character recognition system by using the blocked features and Random Forest Classifier. The recognition rates of Myanmar digits and characters are increased to 96.8% and 92.6% respectively with Random Forest Classifier 2020-06-12 http://hdl.handle.net/20.500.12678/0000007422 https://meral.edu.mm/records/7422