英语翻译请问有没有"Localizing and Segmenting Text in Images and Videos" 这编文章的中文翻译版本?原文如下：Localizing and Segmenting Text in Images and VideosRainer Lienhart,Member,IEEE,and Axel WernickeAbstract—Many images

英语翻译
请问有没有"Localizing and Segmenting Text in Images and Videos" 这编文章的中文翻译版本?
原文如下：
Localizing and Segmenting Text in Images and Videos
Rainer Lienhart,Member,IEEE,and Axel Wernicke
Abstract—Many images—especially those used for page design
on web pages—as well as videos contain visible text.If these text
occurrences could be detected,segmented,and recognized automatically,
they would be a valuable source of high-level semantics
for indexing and retrieval.In this paper,we propose a novel
method for localizing and segmenting text in complex images and
videos.Text lines are identified by using a complex-valued multilayer
feed-forward network trained to detect text at a fixed scale
and position.The network’s output at all scales and positions is integrated
into a single text-saliency map,serving as a starting point
for candidate text lines.In the case of video,these candidate text
lines are refined by exploiting the temporal redundancy of text in video.

本土化和在图像和录象机中的分段本文
下雨 Lienhart ,成员,IEEE 和前外一周半跳 Wernicke
摘要-许多图像-尤其那些二手者对于页设计
在网页上-连同录象机包含看得见的本文.如果这些本文
发生可能自动地被发现,分割,和公开的,
他们会是一个高阶层语意学的有价值来源
因为索引和取回.在这一张纸,我们计画一本小说
在复杂的图像中的本土化的方法和分段本文和
录象机.本文线被藉由使用合成物-尊贵的多层识别
饲养-向前的网络训练在固定的刻度发现本文
而且位置.网络的输出在所有的刻度和位置被整合
进入一张本文-saliency 地图之内,如出发点的一人份餐点
因为候选人本文排成一行.在录象机的情况,这些候选人本文
线被藉由在录象机中开发本文的当时多余精炼

尤其是许多用于图像设计网页,网页内容以及录影带有形文本. If these text occurrences could be detected, segmented, and recognized auto-matically, they would be a valuable source of high-level seman-tics for indexing and retrieval....

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尤其是许多用于图像设计网页,网页内容以及录影带有形文本. If these text occurrences could be detected, segmented, and recognized auto-matically, they would be a valuable source of high-level seman-tics for indexing and retrieval.如果这些文字出现可检测、分割、确认自动油压、他们将宝贵的资源高层以Semans-抽搐索引和检索. In this paper, we propose a novel method for localizing and segmenting text in complex images and videos.在这篇文章中,我们提出了一种复杂的图像本地化和分割文本和录像. Text lines are identified by using a complex-valued multi-layer feed-forward network trained to detect text at a fixed scale and position.文线确定采用复合值多层前馈网络训练查看全文按固定位置和规模. The network s output at all scales and positions is in-tegrated into a single text-saliency map, serving as a starting point for candidate text lines.该网络的规模和产值在各职位的集成计算机辅助成一个单一文本凸地图作为起点文候选线路. In the case of video, these candidate text lines are refined by exploiting the temporal redundancy of text in video.在涉及视频,这些人选都是文系利用时间冗余成品文本视频. Localized text lines are then scaled to a fixed height of 100 pixels and segmented into a binary image with black characters on white background.文线路局部然后进入一个固定的高度和分割成100个像素值图像白底黑字体. For videos, temporal redundancy is exploited to improve segmentation performance.为录像带,裁员是用来改善时序分割表现. Input images and videos can be of any size due to a true multiresolution approach.输入图象和录像可以因任何大小多一个真实的办法. Moreover, the system is not only able to locate and segment text occurrences into large binary images, but is also able to track each text line with sub-pixel accuracy over the entire occurrence in a video, so that one text bitmap is created for all instances of that text line.此外,该系统不仅能找到并进入大段文字出现的二进制图象也能追踪每文符合亚像素精度在整个发生在录像带、文图是一个创造,使一切好说,文路线. Therefore, our text segmentation results can also be used for ob-ject- based video encoding such as that enabled by MPEG-4.因此,我们的文字也可用于分割结果肥胖-冲孔的视频编码等,使由MPEG-4标准.

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