Due to the enormous technical challenges and wide range of applications, scene text recognition (STR) has been an active research topic in computer vision for years. To tackle this tough problem, numerous innovative methods have been successively proposed, and incorporating linguistic knowledge into STR models has recently become a prominent trend. In this work, we first draw inspiration from the recent progress in Vision Transformer (ViT) to construct a conceptually simple yet functionally powerful vision STR model, which is built upon ViT and a tailored Adaptive Addressing and Aggregation (A$^3$) module. It already outperforms most previous state-of-the-art models for scene text recognition, including both pure vision models and language-augmented methods. To integrate linguistic knowledge, we further propose a Multi-Granularity Prediction strategy to inject information from the language modality into the model in an implicit way, \ie, subword representations (BPE and WordPiece) widely used in NLP are introduced into the output space, in addition to the conventional character level representation, while no independent language model (LM) is adopted. To produce the final recognition results, two strategies for effectively fusing the multi-granularity predictions are devised. The resultant algorithm (termed MGP-STR) is able to push the performance envelope of STR to an even higher level. Specifically, MGP-STR achieves an average recognition accuracy of $94\%$ on standard benchmarks for scene text recognition. Moreover, it also achieves state-of-the-art results on widely-used handwritten benchmarks as well as more challenging scene text datasets, demonstrating the generality of the proposed MGP-STR algorithm. The source code and models will be available at: \url{https://github.com/AlibabaResearch/AdvancedLiterateMachinery/tree/main/OCR/MGP-STR}.

翻译：鉴于巨大的技术挑战和广泛的应用场景，场景文本识别（STR）多年来一直是计算机视觉领域活跃的研究课题。为解决这一难题，研究者们相继提出了众多创新方法，而将语言知识融入STR模型已成为近年来的显著趋势。本文首先从视觉Transformer（ViT）的最新进展中汲取灵感，构建了一个概念简洁但功能强大的视觉STR模型，该模型基于ViT和定制化的自适应寻址聚合（A$^3$）模块构建，其性能已超越包括纯视觉模型和语言增强方法在内的多数先前最佳场景文本识别模型。为整合语言知识，我们进一步提出多粒度预测策略，通过隐式方式将语言模态信息注入模型，即在传统字符级表征之外，引入自然语言处理中广泛使用的子词表征（BPE和WordPiece）到输出空间，且无需采用独立语言模型（LM）。为生成最终识别结果，我们设计了两种有效融合多粒度预测结果的策略。所提出的算法（称为MGP-STR）能够将STR的性能推向更高水平。具体而言，MGP-STR在标准场景文本识别基准测试上实现了平均94%的识别准确率。此外，该算法在手写体基准测试和更具挑战性的场景文本数据集上也取得了最先进结果，充分验证了所提MGP-STR算法的通用性。源代码和模型将开源在：\url{https://github.com/AlibabaResearch/AdvancedLiterateMachinery/tree/main/OCR/MGP-STR}。