FREE: A Fast and Robust End-to-End Video Text Spotter
Zhanzhan Cheng, Jing Lu, Baorui Zou, Qiao Liang, Yunlu Xu, Shiliang Pu, Yi Niu, Fei Wu, Shuigeng Zhou
Abstract
Currently, video text spotting tasks usually fall into the four-staged pipeline: detecting text regions in individual images, recognizing localized text regions frame-wisely, tracking text streams and post-processing to generate final results. However, they may suffer from the huge computational cost as well as sub-optimal results due to the interferences of low-quality text and the none-trainable pipeline strategy. In this article, we propose a fast and robust end-to-end video text spotting framework named FREE by only recognizing the localized text stream one-time instead of frame-wise recognition. Specifically, FREE first employs a well-designed spatial-temporal detector that learns text locations among video frames. Then a novel text recommender is developed to select the highest-quality text from text streams for recognizing. Here, the recommender is implemented by assembling text tracking, quality scoring and recognition into a trainable module. It not only avoids the interferences from the low-quality text but also dramatically speeds up the video text spotting. FREE unites the detector and recommender into a whole framework, and helps achieve global optimization. Besides, we collect a large scale video text dataset for promoting the video text spotting community, containing 100 videos from 21 real-life scenarios. Extensive experiments on public benchmarks show our method greatly speeds up the text spotting process, and also achieves the remarkable state-of-the-art.