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.