Content analysis in document images: a scale space approach
With the growing interest in automatic transformation of paper document to its electronic version, geometric and logical structures have become an active research area for a decade. Nowadays, kernel scale space has been widely adopted as the most promising multi-scale image document analysis method. Yet still, traditional methods using scale space approach has its limitations: they are useful mostly on character extraction and they carry a large computational load. In view of these limitations, this paper proposes a new approach using scale space in order to analyse the composite document content. In the proposed method, scale space transform is used to decompose an image into different scaled objects where the scale value is used for detecting progressively finer objects: text, line drawing, logo, and image, with encouraging results on real-life data.