Local Information Based Overlaid Text Detection by Classifier Fusion

When implemented in hardware, image-processing algorithms should be robust to memory limitations because some hardware architectures may not have memory size as large as the whole frame size. Although this is not generally a problem for low-level processing, higher-level understanding, such as object detection, demands novel solutions because the available information may, in some cases, be very local, e.g., only a partial view of the object could fit in the available memory size. In this paper, we propose a novel hardware-oriented overlaid text detection algorithm that can detect text with height as large as five times the memory size. The algorithm integrates a connected component (CC)-based algorithm with a texture-based machine learning approach. The CC-based algorithm uses character-level features in the horizontal direction whereas the texture-based algorithm extracts block-based features to integrate information from all directions. Furthermore, the texture-based algorithm employs a support vector machine (SVM) to benefit from the strength of machine learning tools. In order to detect text of large font size, we also propose a novel hardware-oriented, height-preserving multi-resolution analysis. Finally, the results of the two classifiers as well as color and edge cues are used for the final pixel-based text/non-text decision