Abstract
With the rapid increase of on-line video resources, there is an urgent demand for text detection and recognition technologies to build content-based video indexing and retrieval systems. Chinese news video texts contain highly condensed and rich information, but the low resolution of videos on the Internet and the complexity of Chinese character structures bring challenges for text detection. In this paper, we present a multi-stage scheme for Chinese news video text detection. We propose an improved Stroke Width Transform (SWT) method by incorporating text color consistency constraint for candidate text blocks generation. Then we use “divide and conquer” strategy to distinguish candidate text blocks into three sub-spaces according to their geometric shapes and size. For each sub-space, a neural network is designed to filter the candidates into text or non-text blocks. Finally, the text blocks are merged into text lines based on the stroke width, color and other heuristic information. Experimental results on self-collected Chinese news video dataset and ICDAR 2013 dataset show that the proposed method is effective to detect both news video captions and scene texts.
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