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Scene Text Recognition (STR) Has Emerged As A Challenging Task In Computer Vision Due To Variations In Font Styles, Illumination, Perspective Distortions, Occlusions, And Complex Backgrounds. Traditional Recognition Methods Often Struggle To Maintain Robustness In Such Unconstrained Environments. To Address These Challenges, We Propose A Novel Approach That Integrates Structure-guided Character Detection With Linguistic Knowledge Modeling For Improved Recognition Accuracy. The System First Employs A Structure-aware Character Detection Mechanism That Leverages Spatial Relationships Between Characters To Generate Reliable Candidate Regions, Reducing The Effect Of Noisy Backgrounds And Distortions. Subsequently, Linguistic Knowledge Is Incorporated Through Lexicon Constraints And Language Models To Refine Recognition Outputs And Enforce Semantic Consistency. This Joint Utilization Of Structural Cues And Linguistic Priors Enables The System To Not Only Detect Characters More Precisely But Also Correct Misclassifications In Ambiguous Scenarios. Experimental Results On Benchmark Scene Text Datasets Demonstrate That The Proposed Method Significantly Outperforms Conventional STR Approaches, Achieving Higher Accuracy And Robustness Under Real-world Conditions.