# Efficient Expiration Date Recognition in Food Packages for Mobile Applications
## 1 Introduction
The model uses EfficientNet as its backbone and integrates the Bidirectional Feature Pyramid Network (BiFPN) for better feature extraction. FCOS_EfficientNET demonstrates high accuracy and real-time performance, with FCOS_EfficientNETb3 achieving a mean average precision (mAP) of 48.5% on the MS COCO dataset [7] and 81.5% accuracy on a custom coral bleaching detection dataset. The study demonstrates the potential of this model for real-time and large-scale monitoring tasks in assessing coral reef health. Ref. [8] proposed a compact and highly accurate real-time edge-AI detector for monitoring chicken health. The detector leverages an improved FCOS-Lite model with MobileNet as the backbone, incorporating a gradient weighting loss function and Complete Intersection over Union (CIoU) loss function to enhance classification and localization accuracy. Knowledge distillation is used to transfer critical information from a larger teacher model to the FCOS-Lite detector, maintaining its compactness without compromising performance. The experimental results demonstrate that the proposed detector achieves a mean average precision (mAP) of 95.1% and an F1-score of 94.2%, operating efficiently at over 20 FPS on a resource-constrained edge-AI-enabled device. The innovative approach ensures low power consumption and minimal bandwidth costs, making it suitable for practical applications in automated poultry health monitoring. The above discussions have proved that the detection models could be deployed on edge devices, whereas the recognition models are not deployed yet. Ref. [9] presented a novel approach for scene text recognition using a single visual model, named SVTR. This method dispenses with sequential modeling and decomposes image text into small patches called character components. The model then employs hierarchical stages of component-level mixing, merging, and combining to recognize characters through a simple linear prediction. Ref. [10] presented a novel approach called PARSeq for scene text recognition (STR). The method leverages Permutation Language Modeling (PLM) to train an ensemble of autoregressive (AR) models with shared weights. PARSeq unifies context-free non-AR and context-aware AR inference, offering state-of-the-art (SOTA) results on various STR benchmarks with an accuracy of 96.0% when trained on real data. [PERSON]. [11] proposed SwinTextSpotter, an end-to-end scene text spotting framework leveraging a Transformer-based architecture. The framework introduces Recognition Conversion (RC) to integrate detection and recognition features, enhancing performance through joint optimization. SwinTextSpotter eliminates the need for character-level annotations or rectification modules and achieves state-of-the-art results across six benchmarks, including multi-oriented, arbitrarily shaped, and multilingual datasets. The discussed recognition models show excellent results in scene text recognition; however, the model size is huge when deploying on computation-constrained devices. In this study, we propose that our optical character recognition (OCR) system operates in two phases: text detection and text recognition. The text detection network employs modified fully convolutional one-stage object detection (FCOS) [12] to locate characters on packages, enclosing them in rectangular boxes. The character recognition network uses a convolutional recurrent neural network (CRNN) [13] trained with CTC loss to recognize characters within these boxes. To address date time format, a date time parser is applied after the date is recognized in the original date format. The experimental results demonstrate that our method achieves high accuracy and fast inference. We propose using this OCR system to tackle the challenge of recognizing expiration dates in daily scenarios. The system, combined with edge devices, is portable, easy to deploy in daily life, and energy efficient. The main contributions of our work are as follows:  
1.