Scene text recognition is a vital task in the computer vision field, which focuses on recognizing and understanding text information from natural scene images.In the past few years, single-vision models have advanced notably in addressing scene text recognition challenges. Nevertheless, as deep learning algorithms evolve, these models, with their simplistic attention mechanisms and architectural designs, struggle to capture all the salient features within characters. Consequently, their accuracy has begun to fall behind the current state-of-the-art models, and they exhibit limited proficiency in handling longer text sequences. To overcome these limitations and to further augment the efficacy of scene text recognition models, this study introduces an enhanced model, SVTR-V3, which builds upon the foundation of the SVTR model. The proposed model refines the architecture of the convolutional layers in the backbone network and incorporates novel chimeric components to sharpen the model’s focus on key character features and to bolster its comprehension of global semantics. Empirical evaluations on various public datasets demonstrate that our method significantly boosts the accuracy of the SVTR model series, bringing it in line with contemporary mainstream standards and enhancing the model’s generalization capabilities. Moreover, this paper offers a visual analysis elucidating the improvements brought about by our method.