A Novel Softmax Regression Enhancement for Handwritten Digits Recognition

What is it about?

Background and Aim: Handwritten Digit Recognition has a wide variety of applications in postal mail order, phone records search, automatic car number plate recognition, and in the medical sector that observed how Machine Learning makes the daily tasks simpler and more efficient. This paper aims to improve the classification accuracy of existing handwritten digit systems, thus improve their efficiency.

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Photo by Mick Haupt on Unsplash

Photo by Mick Haupt on Unsplash

Why is it important?

The proposed system consists of an enhanced decision function by adding a “Bias Probability” Function. The function adds negative weights to the output classes (0-9) that have a high positive bias and add a positive weight to the output classes that have a high negative bias to neutralize the effect of this high negative bias. Therefore, the Bayesian Classifier function has been enhanced thereby improving the accuracy of classification, which will further improve the performance of the multiclass probability categorization.