Scene Text Recognition (STR) is a challenging task that involves recognizing text within images of natural scenes. Although current state-of-the-art models for STR exhibit high performance, they typically suffer from low inference efficiency due to their reliance on hybrid architectures comprised of visual encoders and sequence decoders. In this work, we propose the VIsion Permutable extractor for fast and efficient scene Text Recognition (VIPTR), which achieves an impressive balance between high performance and rapid inference speeds in the domain of STR. Specifically, VIPTR leverages a visual-semantic extractor with a pyramid structure, characterized by multiple self-attention layers, while eschewing the traditional sequence decoder. This design choice results in a lightweight and efficient model capable of handling inputs of varying sizes. Extensive experimental results on various standard datasets for both Chinese and English scene text recognition validate the superiority of VIPTR. Notably, the VIPTR-T (Tiny) variant delivers highly competitive accuracy on par with other lightweight models and achieves SOTA inference speeds. Meanwhile, the VIPTR-L (Large) variant attains greater recognition accuracy, while maintaining a low parameter count and favorable inference speed. Our proposed method provides a compelling solution for the STR challenge, which blends high accuracy with efficiency and greatly benefits real-world applications requiring fast and reliable text recognition. The code is publicly available at https://github.com/cxfyxl/VIPTR.

翻译：场景文本识别（STR）是一项具有挑战性的任务，涉及识别自然场景图像中的文本。尽管当前STR领域的最先进模型表现出色，但由于其依赖由视觉编码器和序列解码器组成的混合架构，通常推理效率较低。本文提出面向快速高效场景文本识别的视觉可置换提取器（VIPTR），在STR领域实现了高性能与快速推理速度间的显著平衡。具体而言，VIPTR采用具有金字塔结构的视觉语义提取器，该结构包含多个自注意力层，同时摒弃了传统的序列解码器。这一设计选择使模型轻量化且高效，能够处理不同尺寸的输入。在中英文场景文本识别的多个标准数据集上的大量实验结果验证了VIPTR的优越性。值得注意的是，VIPTR-T（微型）变体在达到与其他轻量级模型相当的高竞争性精度的同时，实现了最先进的推理速度。而VIPTR-L（大型）变体在保持较低参数数量和良好推理速度的前提下，取得了更高的识别精度。我们提出的方法为STR挑战提供了颇具竞争力的解决方案，兼具高精度与高效率，极大惠及需要快速可靠文本识别的实际应用场景。代码已开源在https://github.com/cxfyxl/VIPTR。