Color text extraction with selective metric-based clustering

Natural scene images usually contain varying colors which make segmentation more difficult. Without any a priori knowledge of degradations and based on physical light reflectance, we propose a selective metric-based clustering to extract textual information in real-world images. The proposed method uses several metrics to merge similar color together for an efficient text-driven segmentation in the RGB color space. However, color information by itself is not sufficient to solve all natural scene issues; hence we complement it with intensity and spatial information obtained using Log–Gabor filters, thus enabling the processing of character segmentation into individual components to increase final recognition rates. Hence, our selective metric-based clustering is integrated into a dynamic method suitable for text extraction and character segmentation. Quantitative results on a public database are presented to assess the efficiency and the complementarity of metrics, together with the importance of a dynamic system for natural scene text extraction. Finally running time is detailed to show the usability of our method.