LOGO: Video Text Spotting with Language Collaboration and Glyph Perception Model

Video text spotting aims to simultaneously localize, recognize and track text instances in videos. To address the limited recognition capability of end-to-end methods, tracking the zero-shot results of state-of-the-art image text spotters directly can achieve impressive performance. However, owing to the domain gap between different datasets, these methods usually obtain limited tracking trajectories on extreme dataset. Fine-tuning transformer-based text spotters on specific datasets could yield performance enhancements, albeit at the expense of considerable training resources. In this paper, we propose a Language Collaboration and Glyph Perception Model, termed LOGO to enhance the performance of conventional text spotters through the integration of a synergy module. To achieve this goal, a language synergy classifier (LSC) is designed to explicitly discern text instances from background noise in the recognition stage. Specially, the language synergy classifier can output text content or background code based on the legibility of text regions, thus computing language scores. Subsequently, fusion scores are computed by taking the average of detection scores and language scores, and are utilized to re-score the detection results before tracking. By the re-scoring mechanism, the proposed LSC facilitates the detection of low-resolution text instances while filtering out text-like regions. Besides, the glyph supervision and visual position mixture module are proposed to enhance the recognition accuracy of noisy text regions, and acquire more discriminative tracking features, respectively. Extensive experiments on public benchmarks validate the effectiveness of the proposed method.

Video text tracking for dense and small text based on pp-yoloe-r and sort algorithm

Although end-to-end video text spotting methods based on Transformer can model long-range dependencies and simplify the train process, it will lead to large computation cost with the increase of the frame size in the input video. Therefore, considering the resolution of ICDAR 2023 DSText is 1080 * 1920 and slicing the video frame into several areas will destroy the spatial correlation of text, we divided the small and dense text spotting into two tasks, text detection and tracking. For text detection, we adopt the PP-YOLOE-R which is proven effective in small object detection as our detection model. For text detection, we use the sort algorithm for high inference speed. Experiments on DSText dataset demonstrate that our method is competitive on small and dense text spotting.