An improved binarization method using inter- and intra-block features for natural images

Binarization of natural images is important for text location and content-based analysis. In this work, a new adaptive method is introduced. It is able to improve the binarization results on the degraded images, such as the complex background, the non-uniform illumination, the variations of text font, size, color, and line orientation. The presented method contains three main stages. Firstly, original threshold of each pixel is calculated to produce some candidate blocks. Secondly, the new inter- and intra-block features are extracted from the candidates based on the characteristics of text. Finally, each block is scored from 0 to s using the mentioned features. The blocks with low scores are considered as subcomponents of background. After extensive experiments, our method demonstrated superior performance against two well-known techniques on the ICDAR 2005 competition dataset.