<instructions>Your goal is to analyze the following comment and estimate how highly it will be upvoted by the Hacker News community.</instructions><comment><author>tgflynn</author><text>This is great work for someone who&amp;#x27;s been &lt;i&gt;dabbling with deep learning&lt;/i&gt;.  I used to work on OCR systems for postal address recognition and for the most part this is how I would have approached the problem.&lt;p&gt;A couple of suggestions:&lt;p&gt;You will almost certainly get better accuracy with a broader array of input fonts.  I&amp;#x27;m not sure how well you can simulate all of the image conditions that might occur in a real system so adding some real images to the training set (if you can find them - but I suspect there are OCR type training sets available these days) would probably help.&lt;p&gt;For the speed problem I like the idea of using a simpler net, probably with a course scale image for pre-detection.  You can set an appropriate detection threshold to avoid false negatives and thereby avoid running the full net on the vast majority of images which won&amp;#x27;t contain anything that looks like a plate number.</text><parent_chain></parent_chain></comment><story><title>Number plate recognition with Tensorflow</title><url>https://matthewearl.github.io/2016/05/06/cnn-anpr/</url></story>
<instructions>Your goal is to analyze the following comment and estimate how highly it will be upvoted by the Hacker News community.</instructions><comment><author>anc84</author><text>I love posts like that. Detailed, educational and criticising itself. Thanks!</text><parent_chain></parent_chain></comment><story><title>Number plate recognition with Tensorflow</title><url>https://matthewearl.github.io/2016/05/06/cnn-anpr/</url></story>