tejas-label-ocr 0.1.1

OCR extraction and text grouping for product/nutrition label images

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 EasyOCR 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

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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.