From 793e85d3361697f0071737b1317dea2097c96979 Mon Sep 17 00:00:00 2001
From: Nick White <git@njw.name>
Date: Wed, 2 Oct 2019 17:56:14 +0100
Subject: Add first image for binarisation-introduction, and improve
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+---
+title: "An Introduction to Binarisation"
+date: 2019-02-11
+draft: true
+categories: [binarisation, preprocessing, image manipulation]
+---
+Binarisation is the process of turning a colour or grayscale image into
+a black and white image. It's called binarisation as once you're done,
+each pixel will either be white (0) or black (1), a binary option.
+Binarisation is necessary for various types of image analysis, as it
+makes various image manipulation tasks much more straightforward. OCR is
+one such process, and all major OCR engines today work on binarised
+images.
+
+Binarisation sounds pretty straightforward, and in the ideal case it is.
+You can pick a number, and go through each pixel in the image, checking
+if the pixel is lighter than the number, and if so declaring it to be
+white, otherwise black.
+
+![Example of basic binarisation](example-01.png)
+
+The first issue with this is deciding what number to pick to determine
+whether a pixel is white or black. This number is called the threshold,
+and the whole process of binarisation can also called thresholding, as
+it's so fundamental to the binarising process. Picking a threshold that 
+is too high will result in too few pixels being marked as black, which 
+in the case of OCR means losing parts of characters, which will make it
+harder for an OCR engine to correctly recognise text. Picking a 
+threshold that is too low will result in too many pixels being marked as 
+black, which for OCR means that various non-text noise will be included 
+and considered by the OCR engine, again reducing accuracy.
+
+( INSERT IMAGES DEMONSTRATING EACH )
+
+If all page images were printed exactly the same way, and scanned the
+same way, we could probably get away with just picking an appropriate
+threshold number for everything. However sadly that is not the case, and
+the variances can be significantly greater for historical documents.
+
+There are various algorithms to find an appropriate threshold number for
+a given page. A particularly well-known and reasonable one is called the
+[Otsu algorithm](https://en.wikipedia.org/wiki/Otsu%27s_method). This
+works by splitting the pixels in the image into two classes, one for
+background and one for foreground, with the threshold calculated to
+minimise the "spread" of both classes. Spread here means how much
+variation in pixel intensity there is, so by trying to minimise the
+spread for each class, the threshold aims to find two clusters of
+similar pixel intensities, one being a common background, the other a
+common foreground.
+
+Otsu's algorithm works well for well printed material, on good paper,
+which has been well scanned, as the brightness of the background and
+foreground pixels is consistent. It works less well for pages which
+been scanned with have uneven lighting, as the background brightness
+may be quite different for one corner of a page than another. It is
+also not too good at handling paper or ink inconsistencies, such as
+blemishes, splotches or page grain, as they may well have parts
+which are darker than the threshold.
+
+( INSERT IMAGES DEMONSTRATING OTSU FAILING ON BAD LIGHTING AND WITH
+  SPLOTCHES IN PAGE BEING BLACKENED )
+
+Both of these criticisms could be addressed by using an algorithm that
+could alter the threshold according to the conditions of the region on
+the page. That will be covered in the next blog post,
+[Adaptive Binarisation]({{< ref "binarisation-adaptive.md" >}}).
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