Abstract
Modern scene text recognition systems often depend on large end-to-end architectures that require extensive training and are prohibitively expensive for real-time scenarios. In such cases, the deployment of heavy models becomes impractical due to constraints on memory, computational resources, and latency. To address these challenges, we propose a novel, training-free plug-and-play framework that leverages the strengths of pre-trained text recognizers while minimizing redundant computations. Our approach uses context-based understanding and introduces an attention-based segmentation stage, which refines candidate text regions at the pixel level, improving downstream recognition. Instead of performing traditional text detection that follows a block-level comparison between feature map and source image and harnesses contextual information using pretrained captioners, allowing the framework to generate word predictions directly from scene this http URL texts are semantically and lexically evaluated to get a final score. Predictions that meet or exceed a pre-defined confidence threshold bypass the heavier process of end-to-end text STR profiling, ensuring faster inference and cutting down on unnecessary computations. Experiments on public benchmarks demonstrate that our paradigm achieves performance on par with state-of-the-art systems, yet requires substantially fewer resources.
Abstract (translated)
现代场景文本识别系统通常依赖于大型端到端架构，这些架构需要大量的训练，并且在实时应用场景中成本高昂。在这种情况下，由于内存、计算资源和延迟的限制，部署重型模型变得不切实际。为了解决这些问题，我们提出了一种新颖的无需训练即插即用框架，该框架利用预训练文本识别器的优势，同时最小化冗余计算。我们的方法采用基于上下文的理解，并引入了注意力机制驱动的分割阶段，在像素级别上细化候选文本区域，从而提升后续识别效果。 不同于传统的文本检测方式（该方式通过特征图与源图像之间进行块级对比来实现，并利用预训练的字幕生成器来获取上下文信息），我们的框架可以直接从场景中提取出单词预测。提取出来的文本会经过语义和词汇层面的评估以获得最终得分。那些达到或超过预定置信度阈值的预测结果将绕过更复杂的端到端文本STR处理流程，从而加快推理速度并减少不必要的计算。 在公开基准测试上的实验表明，我们的方法实现了与现有顶级系统相当的表现，但却需要显著较少的资源。
URL
https://arxiv.org/abs/2503.15639
