End-to-End Video Text Spotting with Transformer (2203.10539v3)

Abstract: Recent video text spotting methods usually require the three-staged pipeline, i.e., detecting text in individual images, recognizing localized text, tracking text streams with post-processing to generate final results. These methods typically follow the tracking-by-match paradigm and develop sophisticated pipelines. In this paper, rooted in Transformer sequence modeling, we propose a simple, but effective end-to-end video text DEtection, Tracking, and Recognition framework (TransDETR). TransDETR mainly includes two advantages: 1) Different from the explicit match paradigm in the adjacent frame, TransDETR tracks and recognizes each text implicitly by the different query termed text query over long-range temporal sequence (more than 7 frames). 2) TransDETR is the first end-to-end trainable video text spotting framework, which simultaneously addresses the three sub-tasks (e.g., text detection, tracking, recognition). Extensive experiments in four video text datasets (i.e.,ICDAR2013 Video, ICDAR2015 Video, Minetto, and YouTube Video Text) are conducted to demonstrate that TransDETR achieves state-of-the-art performance with up to around 8.0% improvements on video text spotting tasks. The code of TransDETR can be found at https://github.com/weijiawu/TransDETR.

End-to-End Video Text Spotting with Transformers: An In-Depth Overview

The presented paper addresses the challenge of video text spotting through a novel method rooted in the transformer sequence modeling paradigm. The proposed framework, named "Trans," offers an end-to-end trainable solution for simultaneously detecting, tracking, and recognizing text instances within video sequences. Unlike classical methods that rely on complex, multi-staged pipelines, Trans adopts a more streamlined approach, emphasizing long-range temporal modeling.

Proposed Methodology

Trans redefines the traditional video text spotting pipeline by equating it to a sequence prediction problem. Essential to this approach are two key innovations:

1. Simple Pipeline: Trans eschews multiple models and hand-crafted strategies. The model is divided into a backbone for feature extraction, a transformer-based encoder-decoder for sequence processing, and a recognition head incorporating a Rotated RoI mechanism that facilitates seamless text recognition.
2. Temporal Tracking Loss with Text Query: The framework introduces a unique concept of "text query" to model relationships across full temporal sequences rather than only adjacent frames. The "text query" allows the smooth tracking of text across multiple frames, minimizing dependence on adjacent frame associations. Furthermore, the temporal tracking loss optimizes text query management across long-duration sequences.

Numerical Results

The paper reports significant improvements over state-of-the-art methods across several datasets:

• An 11.3% gain in video text spotting on the ICDAR2015 Video dataset is highlighted by a notable advancement in the ID F1 metric, demonstrating the robustness and precision of the proposed model in text instance tracking and recognition tasks.
• Detection tasks on the ICDAR2013 Video dataset showed a modest, yet important, improvement with a precision of 80.6%, recall of 70.2%, and an F-measure of 75.0%.

Discussion and Implications

Trans represents a shift towards more cohesive and integrated approaches in video text processing. By removing redundant matching processes and hand-crafted components such as NMS, the model not only simplifies the pipeline but also increases efficiency, achieving faster inference speeds. This paradigm opens new avenues for exploiting transformer models in tasks that benefit from long-term temporal dependencies, such as video retrieval, captioning, and autonomous driving applications.

Importantly, the paper also highlights the potential negative impacts on privacy from automating video text spotting at scale, pointing to the need for responsible deployment frameworks.

Future Prospects

The ongoing evolution in transformer models could further amplify the benefits presented by Trans. Future research might explore the integration of more advanced transformer architectures to manage higher-dimensional sequences more effectively. Additionally, extending this framework to handle more complex text spotting scenarios (e.g., varying scripts, fonts) within dense or low-resolution videos remains a pertinent avenue for exploration.

Overall, the paper makes a strong contribution to video text spotting by revitalizing the detection, tracking, and recognition processes through transformers, while laying the groundwork for future advancements in this area.