In the evolving field of text recognition, enhancing the accuracy of Optical Character Recognition (OCR) systems is crucial for improving digital text processing. This project, "Improving Text Recognition in OCR using BERT and Levenshtein Distance," leverages advanced Natural Language Processing (NLP) techniques to refine text outputs. At the core of this system is the BERT (Bidirectional Encoder Representations from Transformers) model, which enables contextual word prediction to correct misrecognized text by understanding relationships between words within sentences. Additionally, the project employs Levenshtein Distance, a dynamic programming algorithm, to compute the minimal edit distance between predicted and actual text strings, ensuring accurate error correction. The system operates without the need for an external database, providing a streamlined approach to generating highly accurate text recognition outputs. This project aims to deliver a powerful tool for enhancing OCR systems, integrating sophisticated NLP models and dynamic algorithms to improve real-world text recognition applications.