This paper describes the design and implementation of a system that recognizes machine-printed Arabic words without prior segmentation. The technique is based on describing symbols in terms of shape primitives. At recognition time, the primitives are detected on a word image using mathematical morphology operations. The system then matches the detected primitives with symbol models. This leads to a spatial arrangement of matched symbol models. The system conducts a search in the space of spatial arrangements of models and outputs the arrangement with the highest posterior probability as the recognition of the word. The advantage of using this whole word approach versus a segmentation approach is that the result of recognition is optimized with regard to the whole word. Results of preliminary experiments using a lexicon of 42,000 words show a recognition rate of 99.4% for noise-free text and 73% for scanned text.
['Badr Al-Badr', 'Robert M. Haralick']
Segmentation-free word recognition with application to Arabic