A high-speed rotation method for binary document images based on coordinate operation of run data

Yoshihiro Shima; Hiroshi Ohya

doi:10.1117/12.641377

16 February 2006 A high-speed rotation method for binary document images based on coordinate operation of run data

Yoshihiro Shima, Hiroshi Ohya

Proceedings Volume 6064, Image Processing: Algorithms and Systems, Neural Networks, and Machine Learning; 60640G (2006) https://doi.org/10.1117/12.641377
Event: Electronic Imaging 2006, 2006, San Jose, California, United States

Abstract

The rotation of an image is one of the fundamental functions in image processing and is applied to document image processing in the office. A method of image rotation based on digital image data has been developed. This paper assumes the binary digital data, and proposes a method which is different from the traditional one based on pixel data. This method can execute a high-speed rotation of binary image based on coordinate data for the start and the end of the run. Using the proposed method, the image rotation at an arbitary angle can be realized by the real number operation on the run data, which is suited to the general-purpose processor. It is a practically useful method since the processing is fast and less memory capacity is required. In this paper, a discussion is made first on the format of the run data, the number of runs and the data complexity for the binary data. Then the newly devised rotation for the binary image is described. The rotation method is used to perform successively the skew coordinate transformations in the vertical and horizontal directions, to determine the rotated images. Finally, a document image is actually rotated on a conputer. The processing time was examined to demonstrate experimentally the usefulness of the proposed method.

Citation Download Citation

Yoshihiro Shima and Hiroshi Ohya "A high-speed rotation method for binary document images based on coordinate operation of run data", Proc. SPIE 6064, Image Processing: Algorithms and Systems, Neural Networks, and Machine Learning, 60640G (16 February 2006); https://doi.org/10.1117/12.641377

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