Abstract
In recent years, vision transformers with text decoder have demonstrated remarkable performance on Scene Text Recognition (STR) due to their ability to capture long-range dependencies and contextual relationships with high learning capacity. However, the computational and memory demands of these models are significant, limiting their deployment in resource-constrained applications. To address this challenge, we propose an efficient and accurate STR system. Specifically, we focus on improving the efficiency of encoder models by introducing a cascaded-transformers structure. This structure progressively reduces the vision token size during the encoding step, effectively eliminating redundant tokens and reducing computational cost. Our experimental results confirm that our STR system achieves comparable performance to state-of-the-art baselines while substantially decreasing computational requirements. In particular, for large-models, the accuracy remains same, 92.77 to 92.68, while computational complexity is almost halved with our structure.
Abstract (translated)
近年来，带有文本解码器的视觉变换器在场景文本识别（STR）任务中表现出卓越性能，这是因为它们能够捕捉长距离依赖关系和上下文关联，并具有很高的学习能力。然而，这些模型的计算和内存需求较大，限制了其在资源受限的应用中的部署。为了应对这一挑战，我们提出了一种高效且准确的STR系统。具体而言，通过引入级联变换器结构，我们在提高编码器效率方面进行了改进。这种结构在编码过程中逐步减少视觉标记大小，有效地消除了冗余标记并降低了计算成本。实验结果证实，我们的STR系统在与最先进的基准模型保持相当性能的同时，大幅减少了计算需求。特别是对于大型模型而言，在使用我们提出的结构后，精度从92.77降至92.68（变化很小），而计算复杂度几乎减半。
URL
https://arxiv.org/abs/2503.18883
