A Novel Multi-view Object Class Detection Framework for Document Image Content Analysis

Recognition of objects from arbitrary viewpoints embedded document images is a new challenge in content-oriented document image analysis. In this paper, we propose a novel framework for detecting generic objects from arbitrary viewpoints described by varied object appearances. We first model the annotated objects from different viewpoints, and then build an explicit correspondence across multi-view detectors. As a result, multi-view objects from untrained viewpoints can be detected by combining the outputs of the adjacent view detectors. Our experiments on several public datasets give promising results for the experimental object classes.