关键词: 维吾尔文, 文档图像, 单词切分, [K]-means, 连体段判别, 单词拆分

Abstract:

At present, the research on the segmentation of printed Uyghur document images mainly focuses on the segmentation of letters, and there are few literatures on word segmentation. In addition, existing literatures are difficult to deal with punctuation marks, and the words written by splitting are not merged, etc. In this paper, firstly, the document image is projected, and then the gap between all conjoined segments in the text line is clustering analyzed by [k]-means clustering algorithm, and the optimal gap discrimination threshold is obtained. Then all conjoined segments are screened and roughly identified, and combined with the threshold discrimination result of the gap to determine the exact segmentation point of the word and obtain the position information of the segmented written word. In the experiment of 100 selected document images, the results show that the proposed method can effectively remove the influence of punctuation on the segmentation results, accurately merge the segmented written words, and keep the average word segmentation accuracy above 99%.

Key words: Uyghur script, documents image, word segmentation, [K]-means, conjoined segment identification, word splitting