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I wanted to be as 'light' as possible in this demo, so I only took a couple steps during the feature acquisition: When using other types of classifiers, it could help add a bit of feature engineering to help the classifier doing its job and achieve high accuracy. When dealing with images, if you use a CNN this step is often overlooked: CNNs are made on purpose to handle raw pixel values, so you just throw the image in and it is handled properly. I only have a single image per digit, but you're free to draw as many samples as you like: it should help improve the performance of you're classifier. In particular, we have 3 handwritten numbers and the task of our model will be to distinguish which image is what number. The objective of this example is to be able to tell what an handwritten digit is, taking as input a photo from the ESP32 camera. If you are curious about a specific image classification task you would like to see implemented, let me know in the comments: I'm always open to new ideas The task In this post we continue with the examples, replicating a 'benchmark' dataset in Machine learning: the handwritten digits classification. Image classification is where Convolutional Neural Networks really shine, but I'm here to question this settlement and demostrate that it is possible to come up with much lighter alternatives. In the last weeks, though, I undertook a more difficult path that is image classification. I started with accelerometer data classification, then did Wifi indoor positioning as a proof of concept. If this is the first time you're reading my blog, you may have missed that I'm on a journey to push the limits of Machine learning on embedded devices like the Arduino boards and ESP32.

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