| Summary: | 碩士 === 國立交通大學 === 電機與控制工程系 === 91 === Since the number of daily-received paper-based office documents is overwhelming, the development of document image analysis, which converts the paper-based documents into electronic forms becomes increasingly important. During the scanning process, we must assure that the document is in the right orientation to avoid mistakes in the following analysis. We describe two algorithms for skew detection in binary document images. The first method is based on fuzzy c-regression models (FCRM). The second method is based on projection-based interline cross-correlation in the scanned image. Instead of finding the correlation for the entire images, it is calculated over small regions selected randomly. For both two methods, they do not require prior segmentation of the document into text and graphics regions and greatly reduce the complexity of the operation. In this thesis, several image analysis techniques, including run-length smoothing, black-white transition, and operating window selection, are used to broaden the document types for skew detection. A new interpolation technique, which is called edge-directed ratio bilinear interpolation technique, is also presented here.
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