In this paper, we study a method for isolated handwritten or hand-printed character recognition using dynamic programming for matching the non-linear multi- projection profiles that are produced from the Radon transfo...In this paper, we study a method for isolated handwritten or hand-printed character recognition using dynamic programming for matching the non-linear multi- projection profiles that are produced from the Radon transform. The idea is to use dynamic time warping （DTW） algorithm to match corresponding pairs of the Radon features for all possible projections. By using DTW, we can avoid compressing feature matrix into a single vector which may miss information. It can handle character images in different shapes and sizes that are usually happened in natural hand- writing in addition to difficulties such as multi-class similarities, deformations and possible defects. Besides, a comprehensive study is made by taking a major set of state-of- the-art shape descriptors over several character and numeral datasets from different scripts such as Roman, Devanagari, Oriya, Bangla and Japanese-Katakana including symbol. For all scripts, the method shows a generic behaviour by providing optimal recognition rates but, with high computational cost.展开更多
文摘In this paper, we study a method for isolated handwritten or hand-printed character recognition using dynamic programming for matching the non-linear multi- projection profiles that are produced from the Radon transform. The idea is to use dynamic time warping （DTW） algorithm to match corresponding pairs of the Radon features for all possible projections. By using DTW, we can avoid compressing feature matrix into a single vector which may miss information. It can handle character images in different shapes and sizes that are usually happened in natural hand- writing in addition to difficulties such as multi-class similarities, deformations and possible defects. Besides, a comprehensive study is made by taking a major set of state-of- the-art shape descriptors over several character and numeral datasets from different scripts such as Roman, Devanagari, Oriya, Bangla and Japanese-Katakana including symbol. For all scripts, the method shows a generic behaviour by providing optimal recognition rates but, with high computational cost.
