Studies on handwritten Chinese characters

such as those on signature verification and text recognition

have been conducted for many years. The skeleton is a key point in these studies. It reduces redundant information but retains a complete topology structure. Using a thinning algorithm to extract a skeleton from a handwritten Chinese character image is a traditional approach. However

distortions exist in the extracted skeleton primarily because the complex areas are not well detected nor processed. Complex areas are the intersections and junctions of strokes. Considering that characters are saved as static images

a computer cannot recognize the existence of these areas with more than one stroke. The computer still regards these areas as an entirety

so the thinning algorithm does not perform well. To solve distortion

this study proposes a skeleton extraction algorithm based on the optimum local correlation degree for handwritten Chinese characters. A simple and effective method to extract complex areas is designed. This method uses a thinning algorithm to obtain the original skeleton. The points on the skeleton are classified as end

common

and complex points. Complex areas are extracted by detecting connected complex points with an eight-neighbor window. Afterward

the information on complex areas is used to modify the original skeleton. The modification algorithm is based on a strategy involving split and reconstruction. The skeleton is split into several stroke segments because all complex areas are removed. Distortions are also eliminated in the removal. The reconstruction step focuses on the reconnection of stroke segments; it analyzes the relationship among stroke segments to restore the skeleton. The directional relationship is considered. The slope between two end points of a segment may not accurately represent the correct direction because the stroke segments are not always straight. Sub-segments adjacent to a complex area can provide the required directional information. In most cases

two stroke segments that are originally connected possess similar directions. However

in several situations

obtaining the direction is insufficient when determining whether two stroke segments belong to one natural stroke. Consequently

the curvature relationship should also be considered. A concept of local correlation degree is proposed based on the relationship of direction and curvature between sub-segments. The correlation degree is designed to be sensitive to the change in direction. The correlation degrees of any two stroke segments in one complex area are calculated. When two stroke segments share the optimal local correlation degree

they are regarded as a pair of continuous segments. The connection step uses interpolation to restore the removed part between continuous segments. Discontinuous segments are provided a proper extension to prevent an incorrect connection. By connecting the stroke segments

the split skeleton is reconstructed

and distortions are modified. Twenty people are asked to write 600 Chinese character samples for the experiment using different pens. All images are denoised and binarized. The use of the eight-neighbor window to detect complex areas in the skeleton provides a good effect. The number of detected complex zones in the 600 samples is close to the theoretical value

whereas that obtained with the contour method is 2.5 times the theoretical value. Most distortions are modified with the local correlation degree

and the reconstructed skeleton approximates the real topology. With the standard skeleton as a criterion

the accuracy of skeleton extraction is 98.41%. The proposed skeleton extraction algorithm for handwritten Chinese characters uses a strategy involving split and reconstruction. Reconstruction is based on the optimum local correlation degree. The proposed method has two main advantages over other methods. First

complex area detection is considerably improved. Other methods detect complex areas mainly through the analysis of turning points on the contour. Unlike these methods

the proposed method implements detection directly from the skeleton. The method is simple and avoids excessive detection. Second

the stroke extraction algorithm provides a good result on distortion modification. Removing complex areas with distortions and reconnecting stroke segments through interpolation provide an efficient solution. The extracted skeletons retain good shapes

and the position relationships among strokes are correct. To conclude

the proposed stroke extraction method demonstrates high accuracy and processing speed. It is an effective and useful method for applications dealing with handwritten Chinese characters.