D-StaR: A Generic Method for Stamp Segmentation from Document Images

Junaid Younas, Muhammad Zeshan Afzal, Muhammad Imran Malik, Faisal Shafait, Paul Lukowicz, Sheraz Ahmed

Abstract: B This paper presents a novel approach, named D-StaR, for stamp segmentation from scanned document images. The presented approach is generic (applicable to stamps of any color, shape, size, and orientation) and based on deep learning. In particular, it uses Fully Convolutional networks for semantic analysis of documents to extract stamps. The presented approach is evaluated on a publicly available stamp dataset. Evaluation results show that the presented approach outperforms the state-of-the-art methods for stamp segmentation and achieves pixel based precision and recall of 87% and 84%, respectively. Deeper analysis of the evaluation reveals that the presented approach can segment both overlapping and non-overlapping stamps, which was always a problem for existing systems in the literature.