Script Identification Based on HSV Features
Many similar shaped scripts are used all over the world today. Scripts identification with similar shaped characters is one of the difficulties in script identification field and it need to be resolved. However, there are a little report about identification of Central Asian countries and Chinese Minority scripts, which identification of similar scripts. In this paper, a multi-script database was established, which are including 2200 plain document images with different resolution in 11 scripts such as English, Chinese, Arabic, Russian, Uyghur, Mongol, Tibet, Turkish, Kyrgyzstani, Uzbekistani and Tajikistani. Then, HSV features were extracted from each whole page image and they were classified by using BP neural network classifier. After experiment in our system, it is achieved 88.14 % of average identification rate and 99.0 % of highest identification rate in our experiment with the dataset. Experimental results indicated that HSV features were effective feature for identify these scripts.