Title :
A new textual/non-textual classifier for document skew correction
Author :
Zhu, Xiaoyan ; Yin, Xiaoxin
Author_Institution :
Dept. of Comput. Sci. & Technol., Tsinghua Univ., Beijing, China
Abstract :
A robust approach is proposed for document skew detection. We use Fourier analysis and SVM to classify textual areas from non-textual areas of documents. We also propose a robust method to determine the skew angle from textual areas. Our approach achieves good performance on documents with large area of non-textual contents.
Keywords :
document handling; document image processing; image classification; optical character recognition; Fourier analysis; OCR; SVM; document skew detection; documents; non-textual areas; skew correction; textual areas; textual document images; Fourier transforms; IEEE members; Kernel; Optical character recognition software; Polynomials; Robustness; Support vector machine classification; Support vector machines; Text analysis; Virtual colonoscopy;
Conference_Titel :
Pattern Recognition, 2002. Proceedings. 16th International Conference on
Print_ISBN :
0-7695-1695-X
DOI :
10.1109/ICPR.2002.1044768
