Shijian Lu, Lim Tan Chew
Department of Computer Science, National University of Singapore, Singapore.
IEEE Trans Pattern Anal Mach Intell. 2008 Jan;30(1):14-24. doi: 10.1109/TPAMI.2007.1158.
This paper reports an identification technique that detects scripts and languages of noisy and degraded document images. In the proposed technique, scripts and languages are identified through the document vectorization, which converts each document image into a document vector that characterizes the shape and frequency of the conta ned character or word images. Document images are vectorized by using vertical component cuts and character extremum points, which are both tolerant to the variation in text fonts and styles, noise, and various types of document degradation. For each script or language under study, a script or language template is first constructed through a training process. Scripts and languages of document images are then determined according to the distances between converted document vectors and the pre-constructed script and language templates. Experimental results show that the proposed technique is accurate, easy for extension, and tolerant to noise and various types of document degradation.
本文报道了一种用于检测噪声干扰和质量退化的文档图像中的文字和语言的识别技术。在所提出的技术中，通过文档矢量化来识别文字和语言，该方法将每个文档图像转换为一个文档向量，该向量表征了所包含字符或单词图像的形状和频率。通过使用垂直分量切割和字符极值点对文档图像进行矢量化，这两种方法对文本字体和样式的变化、噪声以及各种类型的文档退化均具有耐受性。对于每种研究的文字或语言，首先通过训练过程构建一个文字或语言模板。然后根据转换后的文档向量与预先构建的文字和语言模板之间的距离来确定文档图像的文字和语言。实验结果表明，所提出的技术准确、易于扩展，并且对噪声和各种类型的文档退化具有耐受性。
