earticle

영어

This study compares the performance of various convolutional neural network (CNN) models for building an automated deep learning-based letter screening system targeting letters received by inmates in correctional institutions. The models evaluated include well-known architectures such as MobileNet, ResNet, and Inception, as well as recently proposed lightweight models such as ResMobileNet and IGSe, along with GroupConv and SE. Each model was trained on image data containing the Korean word for "knife" ("칼") to assess performance in terms of accuracy, processing time, and model compactness. A total of 1,197 letter image samples were used in the experiment, including 1,140 images with normal words and 57 images containing the target word. The experimental results showed that the MobileNet model had the shortest processing time, making it suitable for real-time applications, while the IGSe model achieved the highest accuracy, demonstrating optimal performance for letter screening tasks. This study suggests that deep learning-based screening techniques can be effectively applied to enhance digital security in the management of inmate correspondence within correctional institutions.