MolNexTR's integration of ConvNext and Vision-Transformer contributes to its superior performance in molecular structure recognition by leveraging the strengths of both models. The ConvNext model, a variant of ResNet, excels at capturing multi-scale feature representations through its split attention mechanism. This allows MolNexTR to extract local atom information effectively from molecular images. On the other hand, the Vision-Transformer focuses on capturing long-range feature dependencies between image patches using transformer blocks. By combining these two approaches in a dual-stream encoder architecture, MolNexTR can extract both local and global features from molecular images more comprehensively than single-model architectures. This nuanced extraction of features enables MolNexTR to predict atoms and bonds accurately while understanding their layout rules, leading to its superior performance in molecular structure recognition.