The paper presents a novel stroke decomposition approach based on a directional filtering technique for recognizing Chinese characters. 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 new Gaussian function is proposed to overcome the general limitation in extracting stroke segments along some fixed and given orientations. The Gaussian function is designed to model the relationship between the orientation and power response of the stroke segment in the filter output. Then, an optimal orientation of the stroke segment can be estimated by finding the maximal power response of the stroke segment. 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.