Scene text recognition (STR) in natural images remains highly challenging due to the large variations in character appearance across diverse real-world conditions, such as changes in font, color, layout, and background complexity—which hinder model generalization and remain insufficiently explored. To address this issue, we propose a visual prompt-guided differential learning (VPDL) framework designed to improve the generalization capability of STR models without requiring scene-specific fine-tuning. Inspired by the human ability to reference prior visual knowledge when recognizing text, VPDL introduces a set of character-level visual prompts that guide the model in perceiving appearance variations among characters. Built upon these prompts, we develop a local-to-global differential learning strategy that enhances patch-level representations and aligns global features with character cues while preserving scene-specific information. Additionally, to mitigate exposure bias in autoregressive decoding, we replace conventional label inputs with context-aware textual prompts, encouraging the decoder to better utilize textual cues embedded in image features. Extensive experiments on widely used benchmarks and real-world datasets demonstrate the effectiveness of VPDL.