Simple (working) Handwritten Digit Recognition: How To Improve It?

I just wrote this very simple handwritten digit recoginition. Here is 8kb archive with the following code + ten .PNG image files. It works: is well recognized as . In short, each

Solution 1:

Digit recognition can be a quite difficult area. Especially when the digits are written in very different or unclear ways. A lot of approaches have been taken in an attempt to solve this problem, and entire competions are dedicated to this subject. For an example, see Kaggle's digit recognizer competition. This competition is based on the well known MNIST data set. In the forums that are there, you will find a lot of ideas and approaches to this problem, but I will give some quick suggestions.

A lot of people approach this problem as a classification problem. Possible algorithms to solve such problems include, for example, kNN, neural networks, or gradient boosting.

However, generally just the algorithm is not enough to get optimal classification rates. Another important aspect to improve your scores is feature extraction. The idea is to calculate features that make it possible to distinguish between different numbers. Some example features for this dataset might include the number of colored pixels, or maybe the width and the height of the digits.

Although the other algorithms might not be what you are looking for, it is possible that adding more features can improve the performance of the algorithm you are currently using as well.