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Finding blocks of text in an image using Python, OpenCV and numpy As part of an ongoing project with the New York Public Library, I’ve been attempting to OCR the text on the back of the Milstein Collection images. Here’s what they look like: A few things to note: There’s a black border around the whole image, gray backing paper and then white paper with text on it. Only a small portion of the imag
In the last post, I described a way to crop an image down to just the part containing text. The end product was something like this: In this post, I’ll explain how to extract text from images like these using the Ocropus OCR library. Plain text has a number of advantages over images of text: you can search it, it can be stored more compactly and it can be reformatted to fit seamlessly into web UIs
⚠ Please visit the official Dygraphs homepage https://dygraphs.com/ instead. You are reading this on a mirror, which may have outdated, incomplete and/or locally patched information, or as part of the Debian package; links may not work. dygraphs is a fast, flexible open source JavaScript charting library. It allows users to explore and interpret dense data sets. Here's how it works: This JavaScrip
dragtable keeps forgetting my column ordering! If you set an id on your table, dragtable will save all column reorderings for that table in a cookie and replay them the next time you visit the page. If you don’t want it to do this, either 1. don’t set an id or 2. set class="draggable forget-ordering". How does dragtable distinguish a click from a drag? This should only be relevant if you’re using
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