Image Text Detection Using a Bandlet-Based Edge Detector and Stroke Width Transform

In this paper, we propose a text detection method based on a feature vector generated from connected components produced via the stroke width transform. Several properties, such as variant directionality of gradient of text edges, high contrast with background, and geometric properties of text components jointly with the properties found by the stroke width transform are considered in the formation of feature vectors. Then, k-means clustering is performed by employing the feature vectors in a bid to distinguish text and non-text components. Finally, the obtained text components are grouped and the remaining components are discarded. Since the stroke width transform relies on a precise edge detection scheme, we introduce a novel bandlet-based edge detector which is quite effective at obtaining text edges in images while dismissing noisy and foliage edges. Our experimental results indicate a high performance for the proposed method and the effectiveness of our proposed edge detector for text localization purposes.