Lightweight, stateless Python library for extracting structured data from product packaging labels using OCR and LLM-based parsing.
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Features
Stateless & In-Memory: Processes raw image bytes—no disk I/O, ideal for serverless (FastAPI/Flask).
OCR + LLM Powered: Uses PaddleOCR for text extraction and Mistral AI (mistral-small-latest) for structured parsing.
Noise Filtering: Automatically cleans low-confidence OCR text and groups spatial blocks (rows/columns/proximity).
Flexible Auth: Pass api_key directly or via MISTRAL_API_KEY environment variable.
Structured JSON Output: Extracts brand, product name, ingredients, nutrition facts, allergens, and more.
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Installation
pip install tejas-label-ocr—
Usage
Front Label Processing
from label_ocr import process_front_label
with open("product_label.jpg", "rb") as f:
image_bytes = f.read()
result = process_front_label(image_bytes, api_key="your-mistral-api-key")
print(result)
# Output: {"brand": "Nestlé", "product_name": "Organic Oats", "claims": ["Gluten-Free", "Non-GMO"], ...}Nutrition Label Processing
from label_ocr import process_nutrition_label
with open("nutrition_panel.jpg", "rb") as f:
image_bytes = f.read()
result = process_nutrition_label(image_bytes, api_key="your-mistral-api-key")
print(result)
# Output: {"serving_size": "30g", "nutrients": [{"name": "Calories", "value": 120, "unit": "kcal", "%RDA": 6}], ...}— Notes —–
Requires a Mistral AI API key (sign up at Mistral AI).
For best results, use high-resolution images of labels with clear text.
1 maintainer
Metadata
1 maintainer
Release files for tejas-label-ocr 0.1.9.1
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| tejas_label_ocr-0.1.9.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 16.5 kB
Release files / tejas_label_ocr-0.1.9.1.tar.gz
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