A nearest-neighbor chain based approach to skew estimation in document images

Authors:
Yue Lu;Chew Lim Tan
Affiliations:
Department of Computer Science, School of Computing, National University of Singapore, 3 Science Drive 2, Kent Ridge, Singapore 117543, Singapore;Department of Computer Science, School of Computing, National University of Singapore, 3 Science Drive 2, Kent Ridge, Singapore 117543, Singapore
Venue:
Pattern Recognition Letters
Year:
2003

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Abstract

A nearest-neighbor chain (NNC) based approach is proposed in this paper to develop a skew estimation method with a high accuracy and with language-independent capability. Size restriction is introduced to the detection of nearest-neighbors (NN). Then NNCs are extracted from the adjacent NN pairs, in which the slopes of the NNCs with a largest possible number of components are computed to give the skew angle of document image. Experimental results on various types of documents containing different linguistic scripts and diverse layouts show that the proposed approach has achieved an improved accuracy for estimating document image skew angle and has an advantage of being language independent.