Francis K. Tse

Automatic picture orientation detection based on classifier combination

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 Classifiers Confidence Analysis (CCA) is introduced with the definition of concepts such as classifiers confidence and generalized confidence. The classification system finally approached 90% recognition accuracy on a wide unconstrained consumer picture set.

Text vectorization based on character recognition and character stroke modeling

In this paper, a text vectorization method is proposed using OCR (Optical Character Recognition) and character stroke modeling. This is based on the observation that for a particular character, its font glyphs may have different shapes, but often share same stroke structures. Like many other methods, the proposed algorithm contains two procedures, dominant point determination and data fitting. The first one partitions the outlines into segments and second one fits a curve to each segment. In the proposed method, the dominant points are classified as “major” (specifying stroke structures) and “minor” (specifying serif shapes). A set of rules (parameters) are determined offline specifying for each character the number of major and minor dominant points and for each dominant point the detection and fitting parameters (projection directions, boundary conditions and smoothness). For minor points, multiple sets of parameters could be used for different fonts. During operation, OCR is performed and the parameters associated with the recognized character are selected. Both major and minor dominant points are detected as a maximization process as specified by the parameter set. For minor points, an additional step could be performed to test the competing hypothesis and detect degenerated cases.

Document image orientation based on both text and image

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.