Learning local image descriptors for word spotting

The Bag-of-Features paradigm has enjoyed great success in computer vision as well as document image analysis applications. By far the most common approach here is to power the Bag-of-Features pipeline with SIFT descriptors which are then clustered into a visual vocabulary using Lloyd´s algorithm. In contrast to using handcrafted descriptors, many researches have started to use descriptors that have been learned from data. While descriptor learning is common in other computer vision tasks, there has been little work on learning descriptors for document analysis purposes. In this work we propose a descriptor learning pipeline designed for word spotting. Evaluation results on the well known George Washington database demonstrate that word-spotting results can effectively be improved by learning specialized local image descriptors.