Discovering Visual Element Evolutions for Historical Document Dating

Discovering visual elements correlated with temporal information in images is a challenging problem. In this paper, we study this problem with regard to handwritten historical document dating. We propose a novel stroke descriptor based on a scale-invariant log-polar space using the stroke width as the scale factor. Furthermore, the primary stroke shapes in documents are generated and termed stroke shape elements (also called visual elements in this paper). To discover the changes in visual elements over time, the Evolutionary Self-Organizing Map (ESOM) is proposed with a new time dimension based on the standard Kohonen's map to preserve the time topology. The proposed ESOM is a weakly-supervised learning method, integrating the visual elements mining and the evolution learning into one framework to preserve the date topology and time topology simultaneously. The dating of historical documents is performed by voting stroke shape elements based on their labels estimated from the the trained ESOM codebook to the label space, yielding a probability distribution. Experimental results demonstrate the effectiveness of the proposed approach for historical document dating.