Explainable Neural Network Recognition of Handwritten Characters

The prevalence of Artificial Intelligence (AI), in many aspects of life, has brought about an increasing need for explainability of AI solutions. Previous work has been posed to interpret the weights of Neural Networks and provide visual hints of explainability. This work introduces a partitioning approach, constructing an explainable architecture that recognizes handwritten characters. The accuracy of the architecture is improved by introducing unexplainable components and a metric to characterize explainability. Ambiguous and mislabeled samples in training data prove challenging. Techniques are applied to identify issues and prune the training set to improve explainability. Pruned samples from the training set are presented to the architecture in an attempt to resolve issues. Results and explainable rationale from pruned data is presented.