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An improved technique to detect text from scene videos

机译:一种从场景视频中检测文本的改进技术

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摘要

It is required to detect text from natural scene videos and images because text carries an essential information. The information such as vehicle license plate numbers and traffic signs are useful in real time traffic surveillance. The information such as shop names, street names, grocery signs, product labels, advertisements, ATM instructions and so on are useful to assist blind people. Numerous text detection techniques are available. Some of them either do not detect the text or partially detect the text when text is excessively small or large. Several techniques do not detect text accurately due to higher blur or changes in illumination. Moreover, the time taken by existing techniques to detect the text can be reduced and accuracy can be increased further. In this paper, we first present the evaluation of the existing scene text detection techniques based on our identified parameters. Then we present an improved text detection technique to detect text from scene video. The proposed technique increases the text detection accuracy and decreases the computational time compared to Edge-enhanced MSER based text detection technique, especially when text is of different font size and video frames are blur.
机译:由于文本携带必不可少的信息,因此需要从自然场景的视频和图像中检测文本。诸如车牌号码和交通标志之类的信息在实时交通监控中很有用。商店名称,街道名称,杂货店标志,产品标签,广告,ATM指示等信息对于帮助盲人非常有用。可以使用多种文本检测技术。当文本过小或过大时,其中一些不能检测文本或部分检测文本。由于较高的模糊度或照明度变化,几种技术无法准确检测文本。此外,可以减少现有技术检测文本所花费的时间,并且可以进一步提高准确性。在本文中,我们首先根据识别出的参数对现有场景文本检测技术进行评估。然后,我们提出了一种改进的文本检测技术来从场景视频中检测文本。与基于边缘增强的MSER的文本检测技术相比,所提出的技术提高了文本检测的准确性,并减少了计算时间,尤其是当文本具有不同的字体大小并且视频帧模糊时。

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