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.
KEYWORDS: Curium, Feature extraction, Distance measurement, Classification systems, Printing, Simulation of CCA and DLA aggregates, Statistical analysis, Intelligence systems, Digital photography, Scanners
Automatic picture orientation recognition is of great significance in many applications such as consumer gallery
management, webpage browsing, content-based searching or web printing. We try to solve this high-level classification
problem by relatively low-level features including Spacial Color Moment (CM) and Edge Direction Histogram (EDH).
An improved distance-based classification scheme is adopted as our classifier. We propose an input-vector-rotating
strategy, which is computationally more efficient than several conventional schemes, instead of collecting and training
samples for all four classes. Then we research on the classifier combination algorithm to make full use of the
complementarity between different features and classifiers. Our classifier combination methods include two levels:
feature-level and measurement-level. And we present two classifier combination structures (parallel and cascaded) at
measurement-level with a rejection option. As the precondition of measurement-level methods, the theory of Classifier's
Confidence Analysis (CCA) is introduced with the definition of concepts such as classifier's confidence and generalized
confidence. The classification system finally approached 90% recognition accuracy on a wide unconstrained consumer
picture set.
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