Handwritten Arabic and Roman word recognition using holistic approach

The research community considers handwritten word recognition (HWR) as an open research problem to date. The reasons behind this are variations in intra-/interpersonal writing style, overlapping and/or touching characters in a word, degraded scanned document images, etc. Two major approaches, namely holistic and analytical, are followed by the researchers while designing an HWR system. In this work, we have followed the holistic approach as it works well on limited and pre-defined lexicon as compared to the analytical approach. As observed in the literature related to handwritten word recognition, irrespective of the approaches, researchers generally extract various local features from hypothetically partitioned segments of a word image while dealing with the said problem. However, no such work has been found which has considered inter-segment similarity that might carry some distinct information about different patterns (here, word segments). To this end, in the present work, we have used Hausdorff and Frechet distances to quantize the similarity among all possible word segments taking two at a time. Along with this, conventional chain code histogram (a shape-based feature descriptor) and modified negative refraction-based shape transformation features have been used. Finally, a majority voting schema is used to combine outputs from six different classifiers. The model has been evaluated on two standard databases, namely IAM and IFN/ENIT, and the results obtained are promising in comparison with state-of-the-art holistic word recognition methods. Moreover, a performance comparison of the present method with some deep learning models confirms the usefulness of the proposed method.