Isolated Handwritten Character Recognition of Ancient Hebrew Manuscripts

Abstract

Character recognition is widely considered an essential factor in preserving and digitizing historical handwritten documents. While it has shown a significant impact, the character recognition of historical handwritten documents is still a challenging task. This work aims to present a study on building a character recognition system for a handwritten ancient Hebrew text utilizing convolutional neural networks, dealing with material degradation, script complexity, and varied handwriting style. Our research underlined the importance of creating a ground-truth dataset for a robust and reliable character recognition system. Moreover, this study compares the performance of four convolutional neural network models applied to our dataset.