Comprehensive Study on Digitization of Handwritten Documents using OCR and LLM

Abstract

Digitization and preservation of historical documents is a complex yet necessary task, often hindered by document degradation, fragmented content, and missing text. This comprehensive study introduces an innovative framework that synergizes Image Processing, Optical Character Recognition (OCR), and Large Language Models (LLMs) to effectively confront these issues. For initial cases, we deploy superior image-processing algorithms such as the Convolutional Neural Network algorithm for denoising degradation so that after being degraded, high-resolution text, more precisely enhanced extracted images yield significant improvement accuracy, making retrieval feasible even where such a set would have appeared otherwise illegible and irrecoverable. We use LLMs such as GPT and BERT on this basis: they are efficient at reconstructing text that contains damage and are good at rebuilding missing content so that coherent text is derived in line with the intent that originally existed for the content involved. This helps integrate manual, hand-based solutions with automated forms of restoration within an interdisciplinary means that allows easier and more consistent preservation of these historical records.

Our findings hold great importance because they are directly related to the progress of historical research methodologies, the saving of our cultural heritage, and modern archiving systems. This work takes fragmented physical records and transforms them into accessible digital repositories, pointing out the critical role that technology plays in the preservation of our historical legacy for future generations and leading down the path to further openness and engagement with our shared cultural heritage.