Text-background decomposition for thai text localization and recognition in natural scenes

Abstract

Thai text localization and recognition in natural scenes is still a grand challenge in current applications. However, the efficiency of recognition rates depends on text localization, i.e., the higher purity of text-background decomposition leads to the higher accuracy rate of character recognition. In order to achieve this purpose, the text-background decomposition methods, namely adaptive boundary clustering (ABC) and n-point boundary clustering (n-PBC), are proposed to improve a precision of text localization. These methods are evaluated by self-entropy for purity measure. Based on 300 test images, the experimental results demonstrate that the ABC method achieves the very low self-entropy, i.e., the low self-entropy implies the good decomposition of text and background. Furthermore, based on 8,077 characters in natural scene test images, the ABC method helps increase the precision of text localization and improves the accuracy rate of character recognition, when compared to the conventional methods.