Decomposing Chinese characters into stroke segments using SOGD filters and orientation normalization

The paper presents a novel directional feature extraction approach based on a directional filtering technique for Chinese character recognition. The proposed filtering technique uses a set of the second-order Gaussian derivative (SOGD) filters to decompose a character into a number of stroke segments. Moreover, a Gaussian function is used to extract the stroke segments along arbitrary orientations. The optimal orientation of each stroke segment can be estimated by finding the maximal power response of the stroke segment from the Gaussian junction. Finally, the effects of decomposition process are analyzed using some simple structural and statistical features extracted from the stroke segments. Experimental results indicate that the proposed SOGD filtering-based approach is very efficient to decompose noisy and degraded character images into a number of stroke segments along an arbitrary orientation. Furthermore, the recognition performance from the application of decomposition process can be improved about 17.31% in test character set.