| AI-Powered OCR for Handwritten Documents with Low Quality and Degradation | |
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MSRDG International Journal of Computer Scientific Technology & Electronics Engineering
© 2025 by MSRDG IJCSTEE Journal Volume 1 Issue 2 Year of Publication: 2025 |
Paper Download Article ID MSRDG-IJCSTEE-V1I2P102 |
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Abstract: An approach that uses AI and OCR to digitize and transform ancient, handwritten registered papers into digital representations that are easily accessible. The system seeks to precisely identify and transcribe text from a variety of handwritten sources by combining cutting-edge optical character recognition (OCR) and natural language processing (NLP) techniques. To guarantee widespread accessibility, regional language support is also included. By providing historical records in an organized digital format, this project improves accessibility while addressing preservation-related issues. The suggested solution increases recognition accuracy for different handwriting styles by utilizing character segmentation techniques and deep learning models. Better transcription performance is ensured by the AI model's ability to adjust to handwritten text irregularities through the use of a strong dataset and ongoing training. Reliance on physical records is further decreased by incorporating cloud-based storage solutions, which facilitate effective document. This digitalization strategy improves data security and lifespan in addition to making historical documents easier to retrieve. The system's usability is expanded by its multilingual capability, which enables papers to be translated and transcribed into multiple regional languages. In order to promote knowledge preservation and historical recording, the solution seeks to offer smooth accessibility to scholars, researchers, and the general public through the use of a simple user interface. Furthermore, by transforming ancient registered handwritten documents into a format that is easily readable and accessible, the AI and OCR solution seeks to enhance historical records' readability and public access. The method improves the usefulness of ancient documents by tackling issues including damaged paper, intricate handwriting, and faded ink. Communities with a variety of linguistic backgrounds can benefit from digital records improved to the incorporation of regional language support, which increases the accessibility and inclusivity of historical material |
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| Keywords: Natural language processing (NLP), Optical character recognition (OCR), Digitization of handwritten documents, AI-powered text recognition, Preservation of historical records, Deep learning, Multilingual OCR, Public access, and Enhancement of readability | |
