Towards an Explainable Machine Learning Framework for Sketched Diagram Recognition

Abstract

In recent years, machine learning has made significant advancements in various fields, including image recognition. However, the complexity of these models often makes it difficult for users to understand the reasoning behind their predictions. This is especially true for sketch recognition, where the ability to understand and explain the model's decision-making process is crucial. To address this issue, our research focuses on developing an explainable machine learning framework for sketch recognition. The framework incorporates techniques such as feature visualization and feature attribution methods which provide insights into the model's decision-making process. The goal of this research is to not only improve the performance of sketch recognition models but also to increase their interpretability, making them more usable and trustworthy for users.