Text Extraction from Scene Images through Color Image Segmentation and Statistical Distributions

This article proposes a scheme for automatic extraction of text from scene images. We proceed by applying statistical features based color image segmentation procedure to the RGB color scene image. The segmentation separates out homogenous (in terms of color and brightness) connected components (CCs) from the image. We assume these CCs include text components. So, prime intention of this article is to inspect these CCs in order to identify possible text components. Here, a number of shape based features are defined that distinguishes between text and non-text components. Further, during learning, the distribution of these features are considered independently and approximate them using parametric distribution families. Here, we apply a selection for the best fitted distribution using likelihood criterion. The class (text or non-text) distribution is the multiplication of the corresponding feature distributions. Consequently, during testing, the CC belongs to the class that produces the highest class distribution score. Our experiments are on the database of ICDAR 2011 Born Digital Dataset. We have obtained satisfactory performance in distinguishing between text and non-text.