<?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 text]]></title><description><![CDATA[A list of topics that have been tagged with text]]></description><link>https://community.m5stack.com/tags/text</link><generator>RSS for Node</generator><lastBuildDate>Wed, 29 Apr 2026 23:11:29 GMT</lastBuildDate><atom:link href="https://community.m5stack.com/tags/text.rss" rel="self" type="application/rss+xml"/><pubDate>Tue, 17 Jan 2023 04:13:35 GMT</pubDate><ttl>60</ttl><item><title><![CDATA[UnitV2 OCR - License Plate Recognition]]></title><description><![CDATA[Interesting project! I have a UnitV2 (and the older UnitV) as well but only little experience with. My approach would be the following:
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.
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.
]]></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>Tue, 17 Jan 2023 04:13:35 GMT</pubDate></item></channel></rss>