Text detection and localization in natural scene images based on text awareness score

Text detection & localization plays an essential role in finding the textual information from natural scene images that can be used in robot navigation, license plate detection, and wearable applications. In this work, we present text detection and localization approach based upon a novel text awareness model that encompasses an improved fast edge preserving and smoothing Maximum Stable Extremal Region (FEPS-MSER) algorithm which uses the fast guided filter to separate the interconnected characters efficiently by removing the mixed pixels around the edges of blurred images. The fast guided filter takes less execution time as compared to other edge-smoothing filters. The combination of five independent and class determining facets namely stroke width deviation, 8-histogram of edge gradients, color variation, occupation ratio, and occupy rate convex area is proposed to differentiate between text and non-text components. The probability of a component to be text is based on Text Awareness Score (TAS) that is calculated by fusing these facets in Naive Bayes using the observation possibility and prior probability of text & non-text components. Naïve Bayes classifier helps in accurate and fast determination of the text awareness score and thus helps in the classification of text & non-text components with the help of graph cut algorithm. The text components have been grouped by using the mean-shift clustering algorithm which is a non-parametric technique and does not require the initial knowledge of clusters. The proposed method achieves improved results concerning precision, recall, and f-measure on the ICDAR benchmark datasets for natural scene images.