# Efficient Expiration Date Recognition in Food Packages for Mobile Applications
## 1 Introduction  
Expiration date recognition is an important technology for visually impaired individuals, contributing positively to their daily routines. Globally, there are millions of people with visual impairments [1] who face challenges in identifying critical information on product packaging, such as expiration dates. This limitation can lead to the consumption of expired products, posing health risks and reducing their quality of life. Europe, with its diverse population, has made significant strides in developing advanced technologies. However, the accessibility of expiration date recognition for visually impaired people continues to hold importance. The integration of this technology into everyday tools and devices is vital for enhancing the independence and safety of these individuals. As the continent continues to progress in various sectors, it is important to prioritize the development and implementation of accessible technologies, such as expiration date recognition, to assist visually impaired individuals. Researchers and developers are increasingly dedicating their efforts to creating fast, accurate, and reliable methods for expiration date detection. These innovations not only improve the lives of visually impaired individuals but also contribute to the broader goal of inclusive technology that benefits everyone. In the computer vision field, notably, the convolutional neural network (CNN) yields promising results in practical applications: Ref. [2] developed a deep learning framework with 97.74% accuracy for recognizing expiration dates on product packages. The framework includes three networks: a date detection network, a day-month-year (DMY) detection network, and a recognition network, and it handles 13 date formats and challenging conditions like varying fonts and blurringusing a publicly available dataset called ExpDate. Ref. [3] proposed a novel system to help visually impaired individuals recognize expiration dates on beverage packages. The system captures an image of the product taken by the user, processes it using a Spatial Transformer Network (STN) to handle skewed, misaligned, or partially missing date images, and then utilizes a dedicated convolutional neural network (CNN) for final recognition. The system achieved high recognition rates of 99.42% for images without spaces and 98.44% for images with spaces. Ref. [4] proposed an enhanced method for optical character recognition (OCR) to detect and recognize expiry dates on food packages. This method uses an improved DBNet combined with a Convolutional Block Attention Module (CBAM) to better extract character features in complex contexts. The improved model shows high accuracy and robustness, achieving a character detection accuracy of 97.9% and a recognition accuracy of 97.8%. The system has been successfully deployed on the NVIDIA Jetson Nano, enhancing its practical applicability in the food packaging industry. While the algorithms mentioned above yield excellent detection results, they are characterized by large model sizes, numerous parameters, and significant computational requirements. Consequently, many studies have shifted their focus towards developing lightweight target-detection algorithms. Ref. [5] proposed an advanced method for detecting Litchi leaf diseases and pests. This method employs an improved fully convolutional one-stage object detection network (FCOS) named FCOS-FL, which incorporates G-GhostNet-3.2 as the backbone to create a lightweight model. The model enhances feature extraction using the Central Moment Pooling Attention (CMPA) mechanism, improving the center sampling and central loss by utilizing real target dimensions. FCOS-FL demonstrates high detection accuracy, with specific accuracy rates of 93.2% for _Mayetiola_ sp. and 92% for Litchi algal spot. The system is designed for deployment on embedded devices, offering practical applications in agriculture. Ref. [6] presented an enhanced coral bleaching detection model named FCOS_EfficientNET.