Abstract
Scene text spotting has attracted the enthusiasm of relative researchers in recent years. Most existing scene text spotters follow the detection-then-recognition paradigm, where the vanilla detection module hardly determines the reading order and leads to failure recognition. After rethinking the auto-regressive scene text recognition method, we find that a well-trained recognizer can implicitly perceive the local semantics of all characters in a complete word or a sentence without a character-level detection module. Local semantic knowledge not only includes text content but also spatial information in the right reading order. Motivated by the above analysis, we propose the Local Semantics Guided scene text Spotter (LSGSpotter), which auto-regressively decodes the position and content of characters guided by the local semantics. Specifically, two effective modules are proposed in LSGSpotter. On the one hand, we design a Start Point Localization Module (SPLM) for locating text start points to determine the right reading order. On the other hand, a Multi-scale Adaptive Attention Module (MAAM) is proposed to adaptively aggregate text features in a local area. In conclusion, LSGSpotter achieves the arbitrary reading order spotting task without the limitation of sophisticated detection, while alleviating the cost of computational resources with the grid sampling strategy. Extensive experiment results show LSGSpotter achieves state-of-the-art performance on the InverseText benchmark. Moreover, our spotter demonstrates superior performance on English benchmarks for arbitrary-shaped text, achieving improvements of 0.7\% and 2.5\% on Total-Text and SCUT-CTW1500, respectively. These results validate our text spotter is effective for scene texts in arbitrary reading order and shape.
Abstract (translated)
场景文本检测近年来引起了相关研究人员的极大兴趣。大多数现有的场景文本检测器遵循先检测后识别的范式，在这种范式中，传统的检测模块很难确定阅读顺序，并导致了识别失败。在重新思考自动回归的场景文本识别方法之后，我们发现一个训练良好的识别器可以在没有字符级检测模块的情况下隐含地感知到完整单词或句子中所有字符的局部语义知识。局部语义知识不仅包括文本内容，还包括正确的阅读顺序中的空间信息。受上述分析的启发，我们提出了由局部语义引导的场景文本检测器（LSGSpotter），它在局部语义的指导下自动回归地解码字符的位置和内容。具体来说，在LSGSpotter中提出了两个有效的模块：一方面，设计了一个起点定位模块（SPLM）来确定文本的起始点并决定正确的阅读顺序；另一方面，提出了一种多尺度自适应注意力模块（MAAM），用于在局部区域内自适应地聚合文本特征。总之，LSGSpotter能够在不受复杂检测限制的情况下完成任意阅读顺序的识别任务，并通过网格采样策略减轻了计算资源的成本。广泛的实验结果表明，LSGSpotter在InverseText基准测试中达到了最先进的性能。此外，在针对任意形状文本的英语基准上，我们的检测器也表现出色，分别在Total-Text和SCUT-CTW1500上实现了0.7%和2.5%的改进。这些结果验证了我们的文本检测器对于任意阅读顺序和形状的场景文本是有效的。
URL
https://arxiv.org/abs/2412.10159
