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Machine Vision Lecture 13 Document Image Analysis Based on lectures of Henry S. Baird Palo Alto Research Center A walking tour of the Document Image Analysis research field Machine reading of text,

A common problem is how to separate text from non-text information in a digital image file. This is a classic problem, especially in an era where images are used for many purposes. This is especially easy to do in a TIFF file because the information needed for this separation (in the form of color) is already included in the image file itself (the image has been converted from the TIF file format, using lib TIFF). The challenge is finding the column for text for such an image. I first learned about the problem of separating text from the background (background information) of the image in the TRC course: Then, in the Spring of 2013, the Stanford University library department started to offer a course on digital library science, called Digital Image Analysis, which is now at end of the syllabus. That same year, I took the Digital Image Analysis course and worked directly with the paper, “Separating Text from Background with Rasterized JPEGs” by Henry S. Baird, which was the first of many papers on the topic, including this one. The paper is really well written, which is why I wanted to use it even today during the course. It is written in a way that, even though you are reading about a problem, you really don't have to take any particular strategy to solve it! In particular, this is a really great introductory paper to the world of document analysis. They introduce the following concepts: Suffix extraction. Determining the column for text (columns are not named from the image). Sorting into different classes: regular, sparse, dense, low quality. A new approach to text classification: regular-text classifier. Saving the output of the classification onto an S3 bucket. Determining the best S3 buckets (more on this another day, after the lectures). This is actually a pretty basic paper, but after I had read about it, I was hooked. The key thing in the paper was the suffix extraction problem, which is quite interesting, as there actually are a lot of different ways to extract an extension from a text that doesn't map directly to a glyph. In particular, suffixes can be extracted from a document either by matching a sequence of characters such as the symbol, or by converting each string that you are matching on to a character, by applying a suffix extractor.

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