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arXiv · 2107.11800

Arbitrarily Shaped Scene Text Detection: A Decade of Advances and Systematic Analysis

Pengwen Dai·Feiyang He·Chaolang Li·Xugong Qin·Wenqi Ren·Xiaochun Cao
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Abstract

Scene text detection has become an important research area in computer vision. However, dynamic changes in scenes and the complex diversity of text appearances make accurate scene text detection highly challenging. Although numerous arbitrary-shaped scene text detection methods have been proposed in recent years, with most claiming state-of-the-art performance, these performance comparisons are often unfair due to various inconsistent settings (e.g., training data, backbones, multi-scale feature fusion, evaluation protocols, etc.). Such discrepancies tend to obscure the strengths and weaknesses of the core techniques being proposed, further hindering progress in the field. In this paper, we first review the development of scene text detection in the deep learning era, systematically tracing and summarizing the technical evolution of the field. Then, we carefully examine and analyze the aforementioned inconsistent settings and propose unified frameworks for bottom-up and top-down scene text detection methods, respectively. Under the unified frameworks, we keep the settings of non-core modules consistent and focus on exploring representations of arbitrary-shaped scene text, aiming to standardize future research and ensure fair comparisons. Finally, we discuss valuable future research directions, with the goal of inspiring subsequent researchers. As the first comprehensive survey dedicated to arbitrary-shaped scene text detection, this paper seeks to eliminate the barriers to performance comparison among existing methods through investigation and detailed analysis, to reveal the strengths and weaknesses of prior models under fair comparisons, and thereby better promote the development of the field.

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Pengwen Dai, Feiyang He, Chaolang Li, Xugong Qin, Wenqi Ren, Xiaochun Cao. 2021-07-25. Arbitrarily Shaped Scene Text Detection: A Decade of Advances and Systematic Analysis. https://arxiv.org/abs/2107.11800

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