Myanmar WarningBoard Recognition System

Abstract

In any country, warning text is described on the signboards or wallpapers to follow by everybody. This paper present Myanmar character recognition from various warning text signboards using block based pixel count and eight-directions chain code. Character recognition is the process of converting a printed or typewritten or handwritten text image file into editable and searchable text file. In this system, the characters on the warning signboard images are recognized using the hybrid eight direction chain code features and 16-blocks based pixel count features. Basically, there are three steps of character recognition such as character segmentation, feature extraction and classification. In segmentation step, horizontal cropping method is used for line segmentation, vertically cropping method and bounding box is used for connected component character segmentation. In the classification step, the performance accuracy is measured by two ways such as KNN (K’s Nearest Neivour) classifier and feature based approach of template matching on 150 warning text signboard images.