NDLI: Video text detection and recognition: Dataset and benchmark

Content Provider	IEEE Xplore Digital Library
Author	Phuc Xuan Nguyen Kai Wang Belongie, S.
Copyright Year	2014
Description	Author affiliation: Google Inc., Mountain View, CA, USA (Kai Wang) \|\| Cornell NYC Tech, Cornell Univ., Ithaca, NY, USA (Belongie, S.) \|\| Dept. of Comput. Sci. & Eng., Univ. of California San Diego, La Jolla, CA, USA (Phuc Xuan Nguyen)
Abstract	This paper focuses on the problem of text detection and recognition in videos. Even though text detection and recognition in images has seen much progress in recent years, relatively little work has been done to extend these solutions to the video domain. In this work, we extend an existing end-to-end solution for text recognition in natural images to video. We explore a variety of methods for training local character models and explore methods to capitalize on the temporal redundancy of text in video. We present detection performance using the Video Analysis and Content Extraction (VACE) benchmarking framework on the ICDAR 2013 Robust Reading Challenge 3 video dataset and on a new video text dataset. We also propose a new performance metric based on precision-recall curves to measure the performance of text recognition in videos. Using this metric, we provide early video text recognition results on the above mentioned datasets.
Sponsorship	IEEE Comput. Soc.
Starting Page	776
Ending Page	783
File Size	1560419
Page Count	8
File Format	PDF
ISBN	9781479949854
DOI	10.1109/WACV.2014.6836024
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-03-24
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Text recognition Measurement Training Smoothing methods YouTube Training data Benchmark testing
Content Type	Text
Resource Type	Article

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