ryan315/document-image-binarization

Project in Data Science 1DL506 - Project 16

Document Image Binarization for Heavily Degraded Swedish Manuscripts

Project Description

Document image binarization aims to separate the foreground text in a document from the noisy background during the preprocessing stage of document analysis. Document images commonly suffer from various degradations over time, rendering document image binarization a daunting task. Typically, a document image can be heavily degraded due to ink bleed-through, faded ink, wrinkles, stains, missing data, contrast variation, warping effect, and noise due to lighting variation during document scanning. Though document image binarization has been extensively studied, their performance for heavily degraded ancient manuscripts is significantly low. This project aims at developing document binarization methods, especially tailored for heavily degraded Swedish texts. Two approaches towards binarisation are investigated: U-Net based and improved DeepOtsu, with a real-world use case of using ancient manuscripts from the 17th-18th century from the National archive in Sweden.