Optical character recognition(OCR) traditionally applies to binary-valued imagery although text is always scanned and stored in gray scale. However, binarization of multivalued image may remove important topological information from characters and introduce noise to character background. In order to avoid this problem, it is indispensable to develop a method which can minimize the information loss due to binarization by extracting features directly from gray scale character images. In this paper, we propose a new method for the direct extraction of topographic features from gray scale character images. By comparing the proposed method with Wang and Pavlidis' method, we realized that the proposed method enhanced the performance of topographic feature extraction by computing the directions of principal curvature efficiently and prevented the extraction of unnecessary features. We also show that the proposed method is very effective for gray scale skeletonization compared to Levi and Montanari's method.
|Number of pages
|IEEE Transactions on Pattern Analysis and Machine Intelligence
|Published - 1995 Jul
Bibliographical noteFunding Information:
‘The authors wish to thank the anonymous reviewers for their helpful comments in improving the earlier draft of this paper. This research was supported by the 1992 Directed Basic Research Fund of Korea Science and Engineering Foundation.
- Gray scale character recognition
- principal curvature
- principal orthogonal elements
- topographic feature extraction
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition
- Computational Theory and Mathematics
- Artificial Intelligence
- Applied Mathematics