Text recognition in natural images remains a challenging yet essential task, with broad applications spanning computer vision and natural language processing. This paper introduces a novel end-to-end framework that combines ResNet and Vision Transformer backbones with advanced methodologies, including Deformable Convolutions, Retrieval-Augmented Generation, and Conditional Random Fields (CRF). These innovations collectively enhance feature representation and improve Optical Character Recognition (OCR) performance. Specifically, the framework substitutes standard convolution layers in the third and fourth blocks with Deformable Convolutions, leverages adaptive dropout for regularization, and incorporates CRF for more refined sequence modeling. Extensive experiments conducted on six benchmark datasets IC13, IC15, SVT, IIIT5K, SVTP, and CUTE80 validate the proposed method's efficacy, achieving notable accuracies: 97.32% on IC13, 58.26% on IC15, 88.10% on SVT, 74.13% on IIIT5K, 82.17% on SVTP, and 66.67% on CUTE80, resulting in an average accuracy of 77.77%. These results establish a new state-of-the-art for text recognition, demonstrating the robustness of the approach across diverse and challenging datasets.

本文针对自然图像中的文本识别难题，提出了一种新颖的端到端框架，结合了ResNet和视觉变换器，并引入可变形卷积、检索增强生成和条件随机场等先进方法，以提升特征表示和光学字符识别（OCR）性能。实验结果表明，该方法在多个基准数据集上达到了新的最佳准确率，显示出其在多样性和挑战性数据集上的鲁棒性和有效性。

基于可变形优化变换器架构的端到端文本识别与检索增强生成