基于深度卷积神经网络的乌尔都语字幕文本结扎识别

U. Hayat, Muhammad Aatif, Osama Zeeshan, I. Siddiqi
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引用次数: 10

摘要

视频中的文本内容包含丰富的信息,可以用于语义索引和后续检索以及视频分析解决方案的开发。基于文本内容的视频检索系统的关键模块包括文本的检测(定位)和文本的识别,而文本的识别是本文的研究课题。更具体地说,本文提出了一种针对乌尔都语文本的字幕文本识别系统。该技术依赖于使用结扎作为识别单位的整体方法。数据驱动的特征提取技术使用了许多预训练的深度卷积神经网络。将网络作为特征提取器,并对所研究的结扎数据集进行微调,实现了较高的结扎识别率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Ligature Recognition in Urdu Caption Text using Deep Convolutional Neural Networks
Textual content in videos contain rich information that can be exploited for semantic indexing and subsequent retrieval as well as development of video analytics solutions. The key modules in a textual content based video retrieval system include detection (localization) of text followed by its recognition, the later being the subject of our study. More specifically, this paper presents a caption text recognition system targeting Urdu text. The technique relies on a holistic approach using ligatures as units of recognition. Data driven feature extraction techniques are employed using a number of pre-trained deep convolution neural networks. The networks are used as feature extractors as well as fine-tuned on the ligature dataset under study and realized high ligature recognition rates.
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