表格文本图像中文字的提取算法

Text Extraction Algorithms for Document Images Including Forms

提出了一种从含有表格的文本图像的页面中提取文字的算法。该算法通过模板扫描形成包围图像前景像素的矩形框 ,从而提取出前景像素 ,进而组合矩形框形成模式链。利用模式的最大黑游程、长、宽三个统计特征实现对模式的分类。实验结果表明 ,该算法不仅对普通的表格有效 ,而且还可以从倾斜的表格及流程图中成功地提取出文字。本算法只适用于二值图像。

A text extraction algorithm is proposed for document images including forms in the page. The foreground pixels are extracted with bounding boxes by mask scanning, then, pattern lists are formed by grouping the neighbored boxes according to a certain criteria. Features such as the maximum black run length, the height, and the width of the patterns are employed for the pattern classification. Experimental results show that the proposed algorithm can successfully tackle normal forms and extract text from skewed forms and the flow chart. The algorithm is valid only for binary document images.