Preserving the Heritage: Evaluating the Character Segmentation Quality in Palm Leaf Manuscripts by Comparing the Classical and Noise2Void Denoising Techniques

The Palm Leaf Manuscripts are a rich source of information about ancient India. It shares an enormous amount of knowledge about the past in terms of art, culture, literature and medicine. As the Manuscripts were developed organically, it is prone to getting damaged very fast. There are many mechanisms used to preserve the physical copies of the manuscripts, but because of the climatic conditions, the deterioration of the manuscripts is inevitable. This work outlines a comparative analysis of classical and deep learning-based approaches for denoising the distorted palm leaf manuscripts based on the segmentation quality of the text inscribed on the PLMs. The traditional pipeline consists of denoising, followed by binarisation and then segmentation of the entire image. We implemented this sequence using both Fast Non-Local Means and a self-trained Noise2Void (N2V) model for denoising. However, the segmented characters, particularly from the Fast NL-based approach, appeared visually distorted. In contrast, the N2V-based difference image showed better structural preservation and closer alignment with the ground truth. To tackle these limitations, we proposed a novel pipeline, which is an innovative processing pipeline that commences with denoising the Palm Leaf Manuscript images using the N2V model, proceeds with direct extraction of the text and culminates in the targeted application of binarisation exclusively on the segmented patches. This restructured approach minimises distortion, enhances text clarity, and preserves character details more effectively. Quantitative evaluation shows improved performance with lower MSE values (0.97, 1.15, 1.02), higher PSNR scores (27.17 dB, 26.61 dB, 29.09 dB) for various binarisation methods, and a structural similarity index (SSIM) of 91%, demonstrating the superiority of the proposed method over the traditional workflow.