Non text eradication from degraded and non degraded videos and images

Shivani Saluja, T. Bedwal, Deepti Rana, Radhika Tayal
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Abstract

Text Segmentation of text from degraded document images is a very complex task due to high mutation between the document background and foreground region. Automatic text extraction is one of the basic feature required for content-based video indexing, automated indexing, automated annotation, structuring and retrieval tasks. Text detection from videos demands conversion of entire video into smaller framesets. Further the framesets are binarized to ease the extraction procedure. This in turn is followed by application of detection procedure on the static frames generated from the video. Text detection can lead to extraction of both superficial and embedded text. Embedded text will be the focus of this research paper because a part of the information depicted in the superficial text is already present in the embedded region. The cycle would start from conversion of dynamic video into static frames, followed by application of filters for noise removal, use of basic morphological operation like dilation and erosion, creation of bounding boxes around the textual content and finally removal of the non text region in such a manner that only the textual region in enhanced. The enhanced textual region is retained while the non textual content is eliminated.
非文本根除从退化和非退化的视频和图像
由于退化文档图像背景和前景区域之间的高度变异,文本分割是一项非常复杂的任务。自动文本提取是基于内容的视频索引、自动索引、自动注释、结构化和检索任务所需的基本功能之一。视频中的文本检测需要将整个视频转换成更小的帧集。进一步对框架集进行二值化以简化提取过程。接下来是对视频生成的静态帧应用检测程序。文本检测可以提取表面文本和嵌入文本。嵌入文本将是本研究论文的重点,因为表面文本中描述的一部分信息已经存在于嵌入区域。这个循环将从将动态视频转换为静态帧开始,然后应用滤波器去除噪声,使用基本的形态学操作,如膨胀和侵蚀,在文本内容周围创建边界框,最后以仅增强文本区域的方式去除非文本区域。保留增强的文本区域,同时消除非文本内容。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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