Video text detection and recognition: Dataset and benchmark
Research output: Contribution to journal › Conference article › Research › peer-review
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.
Original language | English |
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Journal | 2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014 |
Pages (from-to) | 776-783 |
Number of pages | 8 |
DOIs | |
Publication status | Published - 2014 |
Externally published | Yes |
Event | 2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014 - Steamboat Springs, CO, United States Duration: 24 Mar 2014 → 26 Mar 2014 |
Conference
Conference | 2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014 |
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Country | United States |
City | Steamboat Springs, CO |
Period | 24/03/2014 → 26/03/2014 |
ID: 302044488