This chapter transitions the brain-inspired sequence learning framework from theoretical development to practical application, focusing on automatic spelling correction in noisy environments such as those generated by optical character recognition (OCR). Spelling correction is essential in text processing tasks across domains like document digitization, search engines, and medical systems, where errors can impact accuracy and usability. The proposed model addresses this challenge using a biologically inspired mechanism that supports fast, accurate corrections without requiring large datasets, extensive training, or specialized hardware. The chapter begins by outlining the importance of real-time spelling correction and the limitations of existing approaches, including dictionary-based, edit-distance, and deep learning methods. It then introduces a brain-inspired model that processes character sequences through mechanisms resembling working and long-term memory, enabling prediction and correction through pattern matching. The model includes scalability features such as parallel processing and vector merging to efficiently handle large datasets. Experimental evaluations using OCR benchmarks demonstrate the model’s superior performance in both speed and accuracy compared to traditional and neural methods, particularly under noisy conditions. Enhancements such as sentence-level correction with contextual information further improve outcomes. The results validate the model’s robustness, adaptability, and suitability for deployment in low-resource environments.