Bag-of-Features Representations for Offline Handwriting Recognition Applied to Arabic Script

Due to the great variabilities in human writing, unconstrained handwriting recognition is still considered an open research topic. Recent trends in computer vision, however, suggest that there is still potential for better recognition by improving feature representations. In this paper we focus on feature learning by estimating and applying a statistical bag-of-features model. These models are successfully used in image categorization and retrieval. The novelty here is the integration with a Hidden Markov Model (HMM) that we use for recognition. Our method is evaluated on the IFN/ENIT database consisting of images of handwritten Arabic town and village names.