# Digitization of Medical Device Displays Using Deep Learning Models: A Comparative Study
## 1 Introduction
[9] evaluated several object detection models for seven-segment display recognition and found that the Cascade Region-Based Convolutional Neural Network (Cascade R-CNN) achieved the best overall performance. Although its classification accuracy was slightly lower than YOLOv3 and CornerNet, it outperformed all other models in precision, recall, and F1-score, demonstrating potential for reliable digit detection. From the analysis of related work, we can observe that traditional processing methods, although effective for specific and controlled environments, may have limitations in generalizing to different display conditions, such as conditions with noise and lighting variations. However, these methods remain efficient and suitable for constrained devices or controlled settings. [PERSON] et al. [9] conducted a direct comparison between deep learning models and the handcrafted approaches analyzed by [PERSON] et al. [6], showing that deep learning achieved superior performance in terms of detection accuracy, although the study did not focus on computational cost or real-time applicability. In scenarios involving small datasets or highly controlled acquisition conditions, simpler algorithms can still perform adequately and often offer advantages in terms of computational efficiency and ease of deployment. Additionally, the assumption that artificial intelligence (AI) systems will perform reliably under diverse real-world conditions may overlook practical issues such as device variability, user operation errors, and inconsistent lighting environments. These challenges must be considered when evaluating the generalizability of AI-driven digitization approaches in home care settings. This work is part of an initial analysis to support the development of a practical application for automatically digitizing measurements from medical device displays intended for use in home care and clinical environments. To support this goal, the present study focuses on identifying the most suitable detection models based on their accuracy and efficiency under controlled conditions, establishing a technical foundation for the forthcoming implementation phase. The current research provides a comparative analysis of several YOLO [16] and SSD [17] models for detecting and classifying digits and acronyms on medical device displays. This study aims to provide insights into the advantages and limitations of each model, offering guidance for selecting the most suitable option based on the specific requirements of the intended application. The analysis also evaluates the computational efficiency of each model to determine their suitability for real-time deployment in resource-constrained environments. The remainder of this document is organized as follows: Section 2 outlines the methodology, including the object detection models, datasets, and implementation details. Section 3 describes the experimental setup, covering the training process and evaluation metrics used to assess model performance. Section 4 presents the experimental results, followed by Section 5, which discusses the findings, key insights, limitations, and potential improvements. Finally, Section 6 concludes the study, summarizing the main contributions.