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Font Classification of English Printed Character using Non-negative Matrix Factorization  

Lee, Chang-Woo (Dept. of Computer Eng. At Kyungpook National Univ)
Kang, Hyun (Dept. of Computer Eng. At Kyungpook National Univ)
Jung, Kee-Chul (College of Information Science at Soongsil Univ)
Kim, Hang-Joon (Dept. of Computer Eng. At Kyungpook National Univ)
Publication Information
Journal of the Institute of Electronics Engineers of Korea CI / v.41, no.2, 2004 , pp. 65-76 More about this Journal
Abstract
Today, most documents are electronically produced and their paleography is digitalized by imaging, resulting in a tremendous number of electronic documents in the shape of images. Therefore, to process these document images, many methods of document structure analysis and recognition have already been proposed, including font classification. Accordingly, the current paper proposes a font classification method for document images that uses non-negative matrix factorization (NMF), which is able to learn part-based representations of objects. In the proposed method, spatially total features of font images are automatically extracted using NMF, then the appropriateness of the features specifying each font is investigated. The proposed method is expected to improve the performance of optical character recognition (OCR), document indexing, and retrieval systems, when such systems adopt a font classifier as a preprocessor.
Keywords
Font classification; NMF; Part-based unsupervised learning; OCR;
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