<?xml version="1.0" encoding="UTF-8"?><rss xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:atom="http://www.w3.org/2005/Atom" version="2.0"><channel><title><![CDATA[Topics tagged with lpr]]></title><description><![CDATA[A list of topics that have been tagged with lpr]]></description><link>https://community.m5stack.com/tags/lpr</link><generator>RSS for Node</generator><lastBuildDate>Sat, 14 Mar 2026 22:31:41 GMT</lastBuildDate><atom:link href="https://community.m5stack.com/tags/lpr.rss" rel="self" type="application/rss+xml"/><pubDate>Invalid Date</pubDate><ttl>60</ttl><item><title><![CDATA[UnitV2 OCR - License Plate Recognition]]></title><description><![CDATA[<p dir="auto">Interesting project! I have a UnitV2 (and the older UnitV) as well but only little experience with. My approach would be the following:<br />
To recognise only a few well known vehicles you can classify the image of the whole plate including car front (and drivers face) instead of the individual numbers/letters on it. This can be done with the V-training or with the online classifier function.<br />
For recognising single numbers/letters you can try to classify individual numbers/letters and then "read" the x-coordinates of the returned boundary boxes in ascending order to convert into string and then compare to a list/database.</p>
]]></description><link>https://community.m5stack.com/topic/4984/unitv2-ocr-license-plate-recognition</link><guid isPermaLink="true">https://community.m5stack.com/topic/4984/unitv2-ocr-license-plate-recognition</guid><dc:creator><![CDATA[holofloh]]></dc:creator><pubDate>Invalid Date</pubDate></item></channel></rss>