A segmentation free Word Spotting for handwritten documents

In this paper, a Word Spotting model is presented, that is motivated by some characteristics of the human visual system. The proposed bio-inspired model works at two different levels. First, a Global Filtering module enables to define several candidate zones. Then, a Refining Filtering module facilitates the selection of good retrieved results. These two modules are based on a process of accumulation of votes resulting from the application of generalized Haar-Like-features. The process does not need the segmentation of documents neither in lines nor in words. The proposed approach is evaluated using the George Washington Database and outperforming state-of-the-art performances.