Handwriting has unique characteristics for everyone, allowing for the classification of gender and age groups through analysis. This paper explores a lightweight model approach for classifying gender and age groups using Korean handwritten character images on edge devices. EfficientNet-Lite0 and MobileNetV3-Large models were converted into the tfLite format and applied to an Android application, where three-character word images were captured and classified using the device’s CPU and GPU. Experimental results showed that gender classification (two categories) achieved a maximum accuracy of 80.03%, while age group classification (five categories) reached 82.37%. When combining gender and age classification, the model achieved a maximum accuracy of 65.42%. This paper discusses the implementation and performance analysis of this lightweight model-based approach and suggests improvements for future performance enhancement.