Italian-OTC-medicines - Over-the-Counter Drug Packaging Dataset

Overview

This dataset consists of high-resolution images of Over-the-Counter (OTC) medication packaging primarily from the Italian market. It is designed for researchers and developers working on computer vision tasks such as Optical Character Recognition (OCR), Object Detection, and Information Extraction within the pharmaceutical and healthcare sectors.

Each image in the dataset is paired with a comprehensive JSON metadata file containing technical pharmaceutical details, identifiers, and usage instructions.

Dataset Structure

The dataset is organized into image files and a structured JSON metadata array. Each entry includes:

• Product Name: Full commercial name (e.g., ABIMONO*1 OV VAG 600MG).
• Image Reference: Filename mapping to the corresponding .jpg image.
• Identifiers: Global trade identifiers including EAN Codes and SKU Codes.
• Technical Details: A nested object containing clinical information extracted from the product's Summary of Product Characteristics (SmPC):
• Therapeutic Indications: What the drug is used for.
• Posology & Administration: Dosage instructions and methods of use.
• Active Ingredients & Excipients: Full chemical composition.
• Contraindications & Side Effects: Safety warnings and adverse reactions.
• Storage & Shelf Life: Conservation requirements (e.g., "Do not store above 30°C").

Data Sample (JSON)

{
  "nome": "ACETAMOL*AD 20CPR 500MG",
  "immagine": "acetamolad-20cpr-500mg.jpg",
  "dettagli": {
    "Indicazioni terapeutiche": "Symptomatic treatment of fever and mild to moderate pain.",
    "Principi attivi": "Paracetamol 500 mg",
    "Scadenza e conservazione": "Store at temperatures below 25°C..."
  },
  "Codice EAN": "023475054",
  "InElastic": true
}

Potential Use Cases

1. Automated Inventory Management: Training models to identify drug boxes in pharmacies or warehouses via EAN/SKU recognition.
2. Health-Tech Assistants: Developing apps that provide patients with dosage and safety warnings by simply photographing a medication box.
3. Cross-Language Mapping: Using the detailed Italian clinical text to train NLP models for medical translation or entity recognition.
4. Accessibility Tools: Assisting visually impaired users by reading aloud the therapeutic indications and expiry dates from the packaging.

Technical Specifications

• Format: Images (.jpg), Metadata (.json).
• Language: Product labels and details are in Italian.
• Scope: Focused on non-prescription (OTC) drugs, including analgesics, antifungals, and supplements.

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Would you like me to translate the specific "dettagli" fields into English within the JSON structure as well, or do you prefer to keep the clinical text in the original Italian?