Automatic Signature Segmentation Using Hyper-Spectral Imaging

In this paper, we propose a method for automatic signature segmentation using hyper-spectral imaging. The proposed method first uses the connected component analysis and local features to segment the printed text and signatures. Secondly, it uses spectral response of text, signature, and background to extract signature pixels. The proposed method is robust, and remains unaffected by color and intensity of the ink, and by any structural information of the text, as the classification relies exclusively on the spectral response of the document. The proposed method can extract signature pixels either overlapping or non-overlapping from different backgrounds like, logos, tables, stamps, and printed text. We used high-resolution hyper-spectral imaging to study and classify 300 documents with varying backgrounds. We evaluated the proposed classification method and compared results with the state-of-the art system. The proposed method outperformed the state-of-the-art system and achieved 100% precision and 84% recall.