This repository contains an R package for Binarizing Images focusing on local adaptive thresholding with the purpose of improving

• Optical Character Recognition (OCR)
• Handwritten Text Recognition (HTR)

• For regular users, install the package from your local CRAN mirror install.packages("image.binarization")
• For installing the development version of this package: remotes::install_github("DIGI-VUB/image.binarization")
• Note that the package requires a compiler with C++17 capabilities

Look to the documentation of the functions

help(package = "image.binarization")

Get an image, put it into gray scale and binarise it using the method by Su

library(magick)
library(image.binarization)
img <- image_read("scan.jpg")
img <- image_convert(img, format = "PGM", colorspace = "Gray")
img
img <- image_binarization(img, type = "su")
img

• Otsu - "A threshold selection method from gray-level histograms", 1979.
• Bernsen - "Dynamic thresholding of gray-level images", 1986.
• Niblack - "An Introduction to Digital Image Processing", 1986.
• Sauvola - "Adaptive document image binarization", 1999.
• Wolf - "Extraction and Recognition of Artificial Text in Multimedia Documents", 2003.
• Gatos - "Adaptive degraded document image binarization", 2005. (Partial)
• NICK - "Comparison of Niblack inspired Binarization methods for ancient documents", 2009.
• Su - "Binarization of Historical Document Images Using the Local Maximum and Minimum", 2010.
• T.R. Singh - "A New local Adaptive Thresholding Technique in Binarization", 2011.
• Bataineh - "An adaptive local binarization method for document images based on a novel thresholding method and dynamic windows", 2011. (unreproducible)
• ISauvola - "ISauvola: Improved Sauvola’s Algorithm for Document Image Binarization", 2016.
• WAN - "Binarization of Document Image Using Optimum Threshold Modification", 2018.

Based on https://github.com/brandonmpetty/Doxa

By DIGI: Brussels Platform for Digital Humanities: https://digi.research.vub.be