GoMatching: A Simple Baseline for Video Text Spotting via Long and Short Term Matching

• URL: http://arxiv.org/abs/2401.07080v1
• Date: Sat, 13 Jan 2024 13:59:15 GMT
• Title: GoMatching: A Simple Baseline for Video Text Spotting via Long and Short Term Matching
• Authors: Haibin He, Maoyuan Ye, Jing Zhang, Juhua Liu, Dacheng Tao
• Abstract summary: Video text spotting presents an augmented challenge with the inclusion of tracking. GoMatching focuses the training efforts on tracking while maintaining strong recognition performance. We set a new record on the ICDAR15-video dataset, and one novel test set with arbitrary-shaped text.
• Score: 63.92600699525989
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Beyond the text detection and recognition tasks in image text spotting, video text spotting presents an augmented challenge with the inclusion of tracking. While advanced end-to-end trainable methods have shown commendable performance, the pursuit of multi-task optimization may pose the risk of producing sub-optimal outcomes for individual tasks. In this paper, we highlight a main bottleneck in the state-of-the-art video text spotter: the limited recognition capability. In response to this issue, we propose to efficiently turn an off-the-shelf query-based image text spotter into a specialist on video and present a simple baseline termed GoMatching, which focuses the training efforts on tracking while maintaining strong recognition performance. To adapt the image text spotter to video datasets, we add a rescoring head to rescore each detected instance's confidence via efficient tuning, leading to a better tracking candidate pool. Additionally, we design a long-short term matching module, termed LST-Matcher, to enhance the spotter's tracking capability by integrating both long- and short-term matching results via Transformer. Based on the above simple designs, GoMatching achieves impressive performance on two public benchmarks, e.g., setting a new record on the ICDAR15-video dataset, and one novel test set with arbitrary-shaped text, while saving considerable training budgets. The code will be released at https://github.com/Hxyz-123/GoMatching.

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