Nowadays, scene text recognition has attracted more and more attention due to its diverse applications. Most state-of-the-art methods adopt an encoder-decoder framework with the attention mechanism, autoregressively generating text from left to right. Despite the convincing performance, this sequential decoding strategy constrains inference speed. Conversely, non-autoregressive models provide faster, simultaneous predictions but often sacrifice accuracy. Although utilizing an explicit language model can improve performance, it burdens the computational load. Besides, separating linguistic knowledge from vision information may harm the final prediction. In this paper, we propose an alternative solution, using a parallel and iterative decoder that adopts an easy-first decoding strategy. Furthermore, we regard text recognition as an image-based conditional text generation task and utilize the discrete diffusion strategy, ensuring exhaustive exploration of bidirectional contextual information. Extensive experiments demonstrate that the proposed approach achieves superior results on the benchmark datasets, including both Chinese and English text images.

翻译：如今，场景文本识别因其多样化的应用而受到越来越多的关注。大多数现有方法采用基于注意力机制的编码器-解码器框架，从左到右自回归生成文本。尽管性能令人信服，但这种顺序解码策略限制了推理速度。相反，非自回归模型提供更快的同步预测，但通常牺牲准确性。虽然利用显式语言模型可以提升性能，但会增加计算负担。此外，将语言知识与视觉信息分离可能损害最终预测。在本文中，我们提出一种替代方案，使用并行迭代解码器，采用先易后难的解码策略。此外，我们将文本识别视为基于图像的条件文本生成任务，并利用离散扩散策略，确保对双向上下文信息进行全面探索。大量实验表明，所提方法在包括中英文文本图像在内的基准数据集上取得了优越结果。