Microsoft, Motorola, Siemens, Hitachi, IAPR, NICI, IUF
We propose an active handwritten Hangul segmentation method. A manageable structure based on Run-length code is defined in order to apply to preprocessing and segmentation. Also three fundamental candidate estimation functions are in- troduced to detect the clues on touching points, and the classification of touching types is attempted depending on the structural peculiarity of Hangul. Our experiments show segmentation performance of 88.2% on touching characters with minimal over-segmentation.