Ligature Recognition in Urdu Caption Text using Deep Convolutional Neural Networks

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

Abstract

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.
基于深度卷积神经网络的乌尔都语字幕文本结扎识别
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。
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