A Unified Framework for Tracking Based Text Detection and Recognition from Web Videos

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OBJECTIVE :

  • Video text extraction plays an important role for multimedia understanding and retrieval. 
  • which pay attention to text tracking using multiple frames, text detection, tracking and recognition.
  • Our model helps to detect the text in web videos

ABSTRACT

  • Video text extraction plays an important role for multimedia understanding and retrieval. A few of recent methods, which pay attention to text tracking using multiple frames, text detection, tracking and recognition. 
  • In this paper, we propose a generic Bayesian-based framework of Tracking based Text Detection And Recognition (T2DAR) from web videos for embedded captions, which is composed of three major components, i.e., text tracking, tracking based text detection, and tracking based text recognition. 
  • In this unified framework, text tracking is first conducted by tracking-by-detection. our proposed approach largely improves the performance of text detection and recognition from web videos.

EXISTING SYSTEM

  • In our existing system, the text has received increasing attention as a key and direct information source in the video. 
  • For examples, caption text usually annotates information concerning where and when and the events in the video happened or who was involved 
  • Hence, text extraction and analysis in the video have attracted considerable attention in multimedia understanding systems. 
  • Scene text, audio, and visual features to construct their retrieval systems. Specifically, some researchers performed investigations of only image retrieval by leveraging both textual image representations

DISADVANTAGES

  • Retrieval of text tracking only in images
  • Low accuracy 
  • Less speed 

PROPOSED SYSTEM

  • Video text extraction plays an important role for multimedia understanding and retrieval.
  • we propose a generic Bayesian-based framework of Tracking based Text Detection And Recognition (T2DAR) from web videos for embedded captions.
  • which is composed of three major components, i.e., text tracking, tracking-based text detection, and tracking-based text recognition. 
  • In this unified framework, text tracking is first conducted by tracking-by-detection.

ADVANTAGES

  • Easily tracking and text detection in web videos
  • High accuracy compared to the existing system 
  • Less time 

SYSTEM ARCHITECTURE


SYSTEM MODULES

Module 1: Dataset collection
Module 2: Text tracking
Module 3: Tracking based Detection 
Module 4: Tracking based Recognition 

SOFTWARE REQUIREMENTS

Operating System           :    Windows
Simulation Tool              :    Opencv python
Documentation               :    Ms – Office

HARDWARE REQUIREMENTS

CPU type                                  :    Intel Pentium 4
Ram size                                   :    512 MB
Hard disk capacity                    :    80 GB
Monitor type                             :    15 Inch colour monitor
Keyboard type                          :     Internet keyboard

REFERENCES

Q. Ye and D. Doermann, “Text detection and recognition in imagery: A survey,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 37, no. 7, pp. 1480–1500, 2018.

Y. Zhu, C. Yao, and X. Bai, “Scene text detection and recognition: Recent advances and future trends,” Frontiers in Computer Science, vol. 10, no. 1, pp. 19–36, 2019.

Location: Chennai, Tamil Nadu, India

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