Document Image Orientation Detection 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 degrees and 270 degrees 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, confidence-based 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.