International Journal of Science, Engineering and TechnologyAuthors: Dr. P. Meenakshi Devi, G. Sandhya S. Dharshini, K. Balambiga
Abstract: This paper presents an advanced deep learning system that takes handwritten text recognition and correction to the next level. It combines powerful computer vision and language modeling techniques to not only read messy or varied handwriting but also clean it up with smart, context-aware corrections. At the heart of the system is a BERT-based Transformer that uses self-attention to understand the structure and flow of handwriting. A CNN first processes the scanned image to pull out visual features, which are then passed through the Transformer to generate readable text. But it doesn’t stop there once the initial text is generated, a language model with autoencoding and contextual awareness steps in to refine the results. It fixes spelling mistakes and grammatical errors by analyzing the full sentence, much like how a human would understand and edit a paragraph. This approach outperforms traditional OCR tools and dictionary-based corrections by adapting to different handwriting styles, languages, and even noisy or low-quality scans. The system is fast, accurate, and flexible, making it a practical solution for tasks like digitizing educational materials, archiving historical documents, managing healthcare records, and streamlining office paperwork. By merging vision and language understanding in a single pipeline, this system offers a smarter way to process handwritten content in the digital age
