Stroke extraction for chinese characters using a trend-followed transcribing technique

Abstract The merit of the stroke extraction algorithms which utilize the thinning process is the ease of the feature abstracting from the skeleton of a character. The two main tasks for this kind of algorithms are to find the certain adjacent segmental strokes for being merged into a complete stroke, and to search the corner point to divide the bend segmental stroke into two or more individual strokes. This paper proposes an intuitive and effective stroke extraction method that passes through the distorted region and gets the reliable information of global features by applying the trend-followed transcribing technique to correctly accomplish the tasks. In our experiments, the most frequently used 1500 Chinese characters printed in both the Ming font and the Fang-Sung font with the size of 64 × 64 points are tested. The results of the experiments show that the rate for correctly extracting all strokes of a character is 97.8% for the Ming font and 98.4% for the Fang-Sung font. That is, the proposed stroke extraction algorithm is useful and reliable.