Otary Now Includes 17 Image Binarization Methods
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Otary, an image processing library, has expanded its capabilities with 17 image binarization methods, sparking interest and appreciation from the HN community for its utility and potential applications.
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I am thrilled to share a new update to Otary, my open-source Python library dedicated to image manipulation and 2D geometry processing.
Otary now includes 17 binarization methods, designed to make experimentation both simple for beginners and powerful for advanced users.
5 basic methods: easily accessible for quick and efficient use: simple, otsu, adaptive, bradley, and sauvola.
These methods are the most classic and effective, perfect for new users and for 90% of practical cases.
12 advanced methods: for users who want to explore, compare, and understand more sophisticated approaches. They are intended for image processing specialists and researchers who want to experiment with new ideas.
The documentation presents a summary table of the 17 methods, classified by year of publication and accompanied by links to the original scientific articles.
This library might have saved me a lot of time recently, as I don't have a working scanner and was forced to photograph and clean up a lot of pages of documents. I used gimp, which required a lot of experimentation. It's only got a couple adaptive tools that can do the job with uneven lighting, but they sometimes require some pre or post processing for good results.
in your experience, which binarization (adaptive or otherwise) do you often practically use?