基于缩放的光学字符识别

V. Anand, Ankit P. Navik
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引用次数: 0

摘要

物联网(IoT)被视为广泛应用的先进范例之一,也是即将到来的信息和通信技术(ICT)的众多设备,从手持设备到智能广告牌(公共显示器)。用图画的方式表达观点的用法急剧增加。目前,视频平面中的文本与其他内容一起起着至关重要的作用。然而,有时即使视频平面上的字符大小需要修改(或缩小),但由于缺乏技术而无法完成。我们提出了一种新颖的方法(放大机制来修改文本大小或缩小)在流/实时视频。有几种可用的缩放技术,但使用它们可能会影响视觉体验(放大会导致内容丢失,放大会降低视频质量)。该方案利用基于光学字符识别的k-NN算法(Optical Character Recognition based algorithm)找到视频平面中包含字符的区域的坐标,并利用该坐标在视频平面外创建纹理以进行放大或缩小操作。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optical Character Recognition based Zooming
Internet of Things (IoT) is seen as one of the advance paradigm in widespread applications, and also in upcoming generations of information and communication technologies (ICT) with numerous devices from handheld to smart bill board (public displays). The use of expressing one’s view in pictorial way has increased drastically. Currently, there are numerous application where text in video plane plays vital trivial role along with other content. However, sometimes even though size of characters in video plane needs modification in or zoom out), It cannot be done due to lack of technique so. We propose a novel to do (Zoom in mechanism to modify text sizes or Zoom out) in streamed/real time video. There are several zooming techniques available but use of them would potentially affect visual experience (Zoom in would result in loss of content and Zoom outwould in degradation in video quality). Proposed solution uses k-NN algorithm (Optical Character Recognition based algorithm) to find coordinates of area which contains characters in video plane and this coordinates can be used to create texture out of video plane to perform zoom in or out operations.
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