A Novel Baseline-independent Feature Set for Arabic Handwriting Recognition

HMM-based analytical methods have been widely used for Arabic handwriting recognition. A key factor influencing the performance of HMM-based systems is the features extracted from a sliding window. In this paper, we propose a novel baseline-independent feature set extracted from a wider sliding window to directly capture the contextual information. This feature set is a combination of center of mass based log-space distribution features and inverse percentile features. Center of mass based log-space distribution features use a normalized histogram to describe the distribution of foreground pixels in different direction and distances with respect to the center of mass. Experiments on the IFN/ENIT database demonstrate the effectiveness of the proposed feature set. Further, this feature set can be combined with some popular baseline-independent features to form a large feature set, which achieves comparable results with several state-of-the-art systems using a simple HMM-based architecture.