DateReg — Expiry Date Recognition System

An end-to-end deep learning pipeline that detects and reads expiry dates from product images, built with YOLOv8 for detection and a CTC-based OCR model for text recognition.

Features

• Object Detection — YOLOv8 locates date regions on product packaging
• OCR — Custom CTC model recognizes date text from cropped regions
• Smart Parsing — Handles various date formats, strips prefixes (EXP, BB, MFG, NSX, HSD), fixes common OCR misreads
• Expiry Evaluation — Color-coded status: green (valid), orange (expiring soon), red (expired)
• Visual Feedback — Bounding boxes with confidence scores drawn directly on the image
• Configurable — All model paths, thresholds, and parameters managed via configs/config.yaml

Architecture

                    ┌─────────────┐
                    │ Input Image │
                    └──────┬──────┘
                           ▼
                    ┌─────────────┐
                    │   Pre-      │
                    │ Processing  │
                    └──────┬──────┘
                           ▼
                    ┌─────────────┐
                    │   Detect    │
                    │   YOLOv8    │
                    └──────┬──────┘
                           ▼
            ┌──────────── CRNN ────────────┐
            │                              │
            │  ┌────────┐    ┌───────────┐    ┌──────────┐
            │  │  CNN   │──▶ │   RNN    │ ──▶│ CTC Loss │
            │  │Feature │    │ (BiLSTM)  │    │          │
            │  │Extract.│    │           │    │          │
            │  └────────┘    └───────────┘    └──────────┘
            │                              │
            └──────────────┬───────────────┘
                           ▼
                    ┌─────────────┐
                    │    Text     │
                    └─────────────┘

Tech Stack

Component	Technology
Detection	YOLOv8 (Ultralytics)
OCR	TensorFlow / Keras + CTC Decoder
Web UI	Streamlit
Date Parsing	python-dateutil
Configuration	YAML

Project Structure

DateReg/
├── app.py                          # Streamlit entry point
├── configs/
│   └── config.yaml                 # Model paths, thresholds, parameters
├── src/
│   ├── detection/
│   │   └── detector.py             # YOLODetector class
│   ├── recognition/
│   │   └── ocr.py                  # OCRRecognizer class (CTC)
│   └── utils/
│       └── date_parser.py          # DateParser class
├── models/
│   ├── yolo/best.pt                # Trained YOLOv8 weights
│   └── ocr/best_model_new.h5       # Trained CTC-OCR weights
├── notebooks/
│   ├── train_yolo.ipynb            # YOLOv8 training notebook
│   └── train_ocr.ipynb             # OCR training notebook
├── .gitignore
├── requirements.txt
└── packages.txt                    # System dependencies (libgl1)

Dataset

Dataset	Total	Train	Val	Test
Date-Synth (text images)	128,510	89,957	25,702	12,851
Products-Synth (product images)	11,860	8,300	2,371	1,187

Results

Detection (YOLOv8):

	Precision	Recall	mAP50	mAP50-95
Training	0.969	0.963	0.981	0.862
Test	0.960	0.963	0.976	0.874

Text Recognition (CTC-OCR):

Metric	Score
CER (Character Error Rate)	0.05
WER (Word Error Rate)	0.19

Quick Start

git clone https://github.com/HieuNTg/Date-Recognition.git
cd Date-Recognition
pip install -r requirements.txt
streamlit run app.py

Configuration

All parameters are centralized in configs/config.yaml:

model:
  yolo:
    confidence: 0.25    # Detection confidence threshold
    padding: 5          # Bounding box padding (px)
  ocr:
    img_width: 224      # OCR input width
    img_height: 64      # OCR input height

date_parser:
  warning_days: 30      # Days before expiry to show warning

Known Limitations

• OCR character set does not include . and - separators (would require retraining)
• Date format parsing defaults to dateutil heuristics — may misinterpret ambiguous formats (e.g., 01/02/2026)