## 1 Introduction  
Blood pressure is one of the most widely used and informative clinical metrics for assessing cardiovascular health [1, 2]. It comprises two key values: systolic pressure, measured during heart contraction, and diastolic pressure, measured during heart relaxation [3]. Regular monitoring of these values enables the early detection of cardiovascular conditions such as hypertension, heart attacks, and strokes [4]. Often referred to as a \"silent killer,\" elevated blood pressure can lead to serious complications without showing obvious symptoms [2], making routine monitoring essential for prevention. This is particularly important given that cardiovascular diseases remain the leading cause of death globally [5].  
The rapid advancement of information and communication technologies has reshaped healthcare delivery through mobile health (mHealth) applications [6]. These applications empower users to track their health data and engage with healthcare providers remotely,promoting proactive and preventive care [7]. Mobile health applications have emerged as a key enabler of accessible and real-time health monitoring, contributing to improved personal health and wellness.  
Despite these technological advances, the process of recording blood pressure readings--whether in clinical settings or through mHealth applications--still relies heavily on manual input. In healthcare facilities, readings are often written on paper before being transferred to digital systems, while users at home typically enter values manually into mHealth applications [8]. This approach is impractical, inefficient, and error-prone, with common issues including incorrect data entry and loss of physical records.  
A potential alternative involves the use of wireless communication technologies, such as Bluetooth or Wi-Fi, to transfer measurements directly from blood pressure monitors to mobile applications. However, such features are uncommon in current commercial devices. According to the British & Irish Hypertension Society, fewer than 3% of the 176 recommended digital blood pressure monitors are equipped with Bluetooth connectivity [8]. Replacing existing monitors with newer models that support wireless transmission would be costly and impractical on a large scale, especially in low-resource settings.  
To overcome these limitations, we propose a deep learning-based solution for automated digit recognition and measurement-type classification (systolic, diastolic, and pulse) from images of blood pressure monitors. Instead of relying on manual input or Bluetooth-enabled devices, users simply take a photo of the monitor's seven-segment display using a smartphone. Our proposed model, integrated into a custom-developed mHealth application, automatically extracts and records the measurements. This approach provides a practical, scalable, and cost-effective alternative to enhance the usability and reliability of digital blood pressure tracking.  
The rest of this paper is structured as follows. Section 2 reviews related works, identifies research gaps, and summarizes our contributions. Section 3 describes the proposed method, including dataset preparation, model training, model quantization, and mHealth application development. The evaluation results and discussion are presented in Section 4. Finally, conclusions are drawn in Section 5.