Self Organizing Features Map with Improved Segmentation to Identify Touching of Adjacent Characters in Handwritten Words
This paper presents an intelligent technique for segmentation of off-line cursive handwritten words particularly on touching characters problem. In this study, Self Organizing Feature Maps (SOM) is implemented to identify the touching portion of the cursive words. The image of the connected characters is preprocessed and the core-zone is detected to overcome ascender and descender of the touched character. Prior to clustering, the pixels of the image are mapped into coordinate system as features vector. These features vector are clustered into three classes: left, right and middle region, and the vertical segmentation is performed using SOM to determine the winner node of middle region. The experiments are conducted using syntactic CCC database. The results show that the proposed algorithm yields promising segmentation output and feasible with other existing techniques.
touching character segmentation self-organizing map cursive handwritten syntactic database
Fajri Kurniawan Amjad Rehman Dzulkifli Mohamad Siti Mariyam Shamsudin
Department of Computer Graphics and Multimedia Universiti Teknologi Malaysia Skudai, Johor, Malaysia 81310
国际会议
2009 Ninth International Conference on Hybrid Intelligent Systems(第九届混合智能系统国际会议 HIS 2009)
沈阳
英文
1-6
2009-08-12（万方平台首次上网日期，不代表论文的发表时间）