Denoising Heterogeneous Malayalam Document Images Through Binarization
S Aromal, George Zachariah, M. Harisankar, Nikhil Narayanan, Ansamma John · 2021
Malayalam is an Indian language with a vast collection of traditional documents from heterogeneous sources like palm-leaf manuscripts containing valuable information on traditional medicine and culture, the majority of which are not available in digital formats. Development of Optical Character Recognition (OCR) systems is essential to digitize such documents for effective utilization and preservation. On account of degradation due to ageing and mishandling, noises such as stains, creases, shadows, uneven illumination, complex backgrounds, bleed-through ink, and faint text, historical documents are particularly difficult to process. The presence of such noises degrades the overall performance of the OCR system, if it is not preprocessed appropriately. This paper proposes a novel approach to denoise the document image in the preprocessing stage using division normalization and Otsu's thresholding to enhance its overall quality. The efficacy of the proposed approach is evaluated on document images with natural and synthetically introduced noise elements and it is observed that this work outperforms the pure Otsu thresholding-based method.