关键词: 文种识别, 纹理特征, 判别分析, 马氏距离, 加权融合

Abstract: Many similar shaped scripts are used all over the world today, script identification with similar shaped characters is difficult task in pattern recognition area, and it is one of the urgently solved problems. However, there are a few reports for identification of Central Asian countries and Chinese Minority scripts, especially for scripts with similar shaped characters. In this paper, firstly, two multi-script document image databases are established, which are including 1, 600 and 2, 200 plain text document images respectively in 11 scripts such as English, Chinese, Russian, Mongol, Arabic, Tibet, Uyghur, Turkish, Uzbekistan, Tajikistan and Kazakhstan. Then, six texture features such as mean, standard deviation, entropy, consistency, third order moment and smoothness are extracted from whole page image respectively, and they are classified using seven different kinds of classifiers. On the basis of finding the sensitivity of each feature for the document image, it is determined the optimal weights suitable for identification of central Asian multilingual scripts after the weighted fusion method is used to extract the fusion features. Finally, they are classified by using different classifier via multi- features weighted coefficient fusion, and it is obtained 99.38% and 95.42% of average identification rate with the two established dataset separately. Experimental results indicate that texture features and weighted fusion texture features can better describe the multi-script document images, and they can effectively classify these 11 kinds scripts mentioned above.

Key words: script identification, texture feature, discriminant analysis, Mahalanobis distance, weighted fusion