Handwritten Text Recognition in Ancient Manuscripts Using Convolutional Neural Networks (CNN)
Abstract
Handwritten Text Recognition (HTR) in ancient manuscripts is a crucial task for preserving and analyzing historical records. Convolutional Neural Networks (CNN) offer a powerful approach to digitizing and transcribing these manuscripts with high accuracy. Traditional HTR methods struggle with challenges such as degraded manuscripts, varying handwriting styles, and inconsistent ink quality. These issues reduce the accuracy of conventional optical character recognition (OCR) systems and rule-based methods. The proposed framework leverages CNN-based classification techniques to enhance the recognition of handwritten historical records. It preprocesses manuscript images using noise reduction and binarization, then applies a CNN model trained on diverse ancient handwriting datasets. The model extracts spatial features and classifies characters with improved accuracy. This approach facilitates efficient digitization of handwritten historical texts, enabling researchers to conduct large-scale academic studies. The method can be integrated into digital libraries, archives, and research institutions to improve access to historical knowledge. Experimental results demonstrate that the CNN-based method significantly outperforms traditional techniques, achieving higher recognition rates and reducing errors in transcription. The proposed model ensures accurate and automated recognition of ancient handwriting, contributing to the preservation and accessibility of historical records.