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A spatial-temporal approach for video caption detection and recognition

机译:视频字幕检测和识别的时空方法

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摘要

We present a video caption detection and recognition system based on a fuzzy-clustering neural network (FCNN) classifier. Using a novel caption-transition detection scheme we locate both spatial and temporal positions of video captions with high precision and efficiency. Then employing several new character segmentation and binarization techniques, we improve the Chinese video-caption recognition accuracy from 13% to 86% on a set of news video captions. As the first attempt on Chinese video-caption recognition, our experiment results are very encouraging.
机译:我们提出了一种基于模糊聚类神经网络(FCNN)分类器的视频字幕检测和识别系统。使用新颖的字幕过渡检测方案,我们可以高精度和高效地定位视频字幕的空间和时间位置。然后,采用几种新的字符分割和二值化技术,我们将一组新闻视频字幕的中文视频字幕识别准确率从13%提高到86%。作为对中文视频字幕识别的首次尝试,我们的实验结果令人鼓舞。

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