deploy: a917c4e

Author: edrogers

.lektor/buildstate

@@ -79,7 +79,7 @@ <h2>Improving Receipt Understanding at Fetch</h2>
     RSVP on <a href="https://www.meetup.com/madison-python/events/305982723/">Meetup</a>
 
   </p>
-  <p><img src="../../../../../static/images/2025-03-13-Improved-Receipt-Understanding-Social-Card-1792x1024.png" alt=""></p>
+  <p><img src="../../../../../static/images/2025-03-13-Improving-Receipt-Understanding-Social-Card-1792x1024.png" alt=""></p>
 <p><strong>Note: this event is hosted at Fetch Rewards Headquarters in Downtown Madison. While the event is free and open to the public, attendees must RSVP for entrance.</strong></p>
 <p><strong><em>Link for Required RSVP:</em></strong> <a href="https://lu.ma/uc9se65d">https://lu.ma/uc9se65d</a></p>
 <p>Fetch uses machine learning to process images of receipts into structured data at scale (over 10 million images per day!). This is a big part of how Fetch can reward consumers at a large scale while providing brands an effective way to promote their products. However, due to the varied quality of these images, extracting this structured data can be challenging. In particular, associating product names with prices, quantities, and other details can be difficult to achieve in some low-quality images. In this talk, Adam will show how the combination of a cutting-edge object-detection ML model along with Graph-theory techniques (all implemented in Python) has led to a measurable increase in accuracy for Fetch’s receipt understanding.</p>