Document image orientation based on both text and image

Yuejia Sun; Changsong Liu; Xiaoqing Ding; Zhigang Fan; Francis Tse

doi:10.1117/12.912984

21 February 2012 Document image orientation based on both text and image

Yuejia Sun, Changsong Liu, Xiaoqing Ding, Zhigang Fan, Francis Tse

Proceedings Volume 8302, Imaging and Printing in a Web 2.0 World III; 83020U (2012) https://doi.org/10.1117/12.912984
Event: IS&T/SPIE Electronic Imaging, 2012, Burlingame, California, United States

Abstract

This paper investigated the problem of orientation detection for document images with Chinese characters. These images may be in four orientations: right side up, up-side down, 90° and 270° rotated counterclockwise. First, we presented the structure of text-recognition-based orientation detection algorithm. Text line verification and orientation judgment methods were mainly discussed, afterwards multiple experiments were carried. Distance-difference based text line verification and confidence based text line verification were proposed and compared with methods without text line verification. Then, a picture-based orientation detection framework was adopted for the situation where no text line was detected. This high-level classification problem was solved by relatively low-level vision features including Color Moments (CM) and Edge Direction Histogram (EDH), with distant-based classification scheme. Finally, confidencebased classifier combination strategy was employed in order to make full use of the complementarity between different features and classifiers. Experiments showed that both text line verification methods were able to improve the accuracy of orientation detection, and picture-based orientation detection had a good performance for no-text image set.

Citation Download Citation

Yuejia Sun, Changsong Liu, Xiaoqing Ding, Zhigang Fan, and Francis Tse "Document image orientation based on both text and image", Proc. SPIE 8302, Imaging and Printing in a Web 2.0 World III, 83020U (21 February 2012); https://doi.org/10.1117/12.912984

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KEYWORDS

Optical character recognition

Image segmentation

Detection and tracking algorithms

Image analysis

Curium

Feature extraction

Statistical analysis

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