In this paper, we present NumLog, an Inductive Logic Programming (ILP) system designed for feature range discovery. NumLog generates quantitative rules with clear confidence bounds to discover feature-range values from examples. Our approach focuses on generating rules with minimal complexity from numerical values, ensuring the assessment of methods that could impact accuracy and comprehensibility. Traditional ILP systems, especially those intersecting with computer vision, struggle with numerical data. This convergence presents unique challenges, often hindering the generation of meaningful insights due to the limited capabilities of conventional ILP systems to handle numerical values. NumLog stands out by incorporating an advanced range discovery mechanism that generates low-complexity rules while maintaining high accuracy and comprehensibility. This enhancement significantly improves interpretability, promoting more effective human-machine learning collaboration. We compare NumLog with the state-of-the-art ILP systems such as NumSynth and Aleph and conduct comprehensive experiments on several datasets. We evaluated our approach by measuring accuracy, precision, F1 score, and rule complexity to demonstrate the effectiveness of the methodology.