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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.
Document analysis, shape, script identification, language identification, clustering, classification, association rules

C. Lim Tan and L. Shijian, "Script and Language Identification in Noisy and Degraded Document Images," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 30, no. , pp. 14-24, 2007.
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